CN112364295B - Method, device, electronic equipment and medium for determining importance of network node - Google Patents

Method, device, electronic equipment and medium for determining importance of network node Download PDF

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CN112364295B
CN112364295B CN202011274768.XA CN202011274768A CN112364295B CN 112364295 B CN112364295 B CN 112364295B CN 202011274768 A CN202011274768 A CN 202011274768A CN 112364295 B CN112364295 B CN 112364295B
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王齐
闫桂英
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Academy of Mathematics and Systems Science of CAS
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Abstract

A method, a device, an electronic device and a medium for determining the importance of network nodes based on restarting two-hop random walk are provided, wherein in the method, a probability transition matrix is determined according to the connection relation and the connection quantity between each node in a target network. And determining the number of the first-hop object nodes and the number of the second-hop object nodes of each node according to the one-way connection relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain a proportion value alpha 1、α2 of the total number of the first-hop object nodes and the total number of the second-hop object nodes, wherein the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes. And determining the steady-state probability of the random walk initial node to walk to each node of the target network according to the preset restart probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix. And determining the relative importance degree of all nodes according to the steady state probability.

Description

Method, device, electronic equipment and medium for determining importance of network node
Technical Field
The disclosure belongs to the field of complex networks, and relates to a method, a device, electronic equipment and a medium for determining the importance of a network node, in particular to a method, a system, electronic equipment and a medium for determining the importance of a network node based on restarting two-hop random walk.
Background
A network is a data structure capable of storing interrelationships between entities, each entity acting as a node of the network, information, data or signal interactions between the entities being constructed as a network. A complex network is a network structure that is composed of a vast number of nodes and intricate relationships between nodes.
There are various types of complex networks in the real world, including: social networks, such as a friend circle network, an associated business network, etc., information networks, such as the world wide web, communication networks, etc., and biological networks, such as brain networks, protein networks, and disease networks, etc.
Determining the importance of nodes in these networks facilitates a deep understanding of these networks for a wide range of applications in the internet and data mining fields, such as information processing and prediction using the importance of each node.
The inventor finds that the following problems exist in the existing application scenario in the process of realizing the present disclosure: the current network node importance measurement method mostly depends on the integrity of a network structure, and if the connected edges in the network are in a missing condition, the prediction method has certain limitation, and the prediction result is not accurate enough.
For example, in an application scenario, documents such as papers, journals or patents generally need to be cited each other, but the cited relation between the documents is complex, the cited relation networks are interwoven with each other, and how to mine documents with high value in the complex document cited scenario is a great technical difficulty.
In another scenario, the power optical fiber network is used as a carrier network for voice, data, video, power grid operation and other different services, and the reliability and the safety of the power optical fiber network are important research subjects. The node is one of the core elements of the network, the importance of the node in the network is different, the importance of the core nodes of the network is higher, if the core nodes are destroyed, the reliability of the network is reduced, and even large-area communication is interrupted. Therefore, the method has important practical value in evaluating the importance of the nodes in the power optical fiber network and exploring the important nodes in the network. On one hand, the reliability and the survivability of the whole network can be improved by protecting the core node with emphasis; on the other hand, in the aspect of network survivability, the protection level of the nodes can be determined according to the importance degree of the nodes, so that the information transmission reliability of power grid operation and management is ensured.
In yet another scenario, the selection of the intersection nodes of the urban complex traffic network has important significance for the structural stability and control effectiveness of the traffic network. In an urban traffic network, the randomness attack may be a traffic accident, a failure of an intersection traffic light, traffic control and the like, and when a key node of the urban traffic complex network is subjected to selective attack, the urban traffic network is in danger of breakdown. The evaluation and selection of the network key nodes are of great significance to the implementation of the regional traffic signal control system.
However, in the above scenarios, the current method for measuring/determining the importance of a network node depends mostly on the integrity of the network structure, and if there is a missing border in the network, the prediction method has a certain limitation, so that the prediction result is not accurate enough.
Disclosure of Invention
First, the technical problem to be solved
The disclosure provides a method, a device, an electronic device and a medium for determining importance of a network node, so as to at least partially solve the technical problems set forth above.
(II) technical scheme
A first aspect of the present disclosure provides a method of determining importance of a network node. The determination method comprises the following steps: and determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in the target network. The above determination method further comprises: and determining the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network according to the one-way connection relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain a proportional value alpha 1、α2 of the total number of the first-hop object nodes and the second-hop object nodes, wherein the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes. The above determination method further comprises: and taking each node in the target network as a random walk initial node, and determining the steady-state probability of the random walk initial node to each node of the target network according to the preset restarting probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix. The above determination method further comprises: the relative importance of all nodes of the target network is determined according to the steady state probability of each node of the target network where the random walk initial node walks.
In an embodiment of the present disclosure, determining a probability transition matrix according to a connection relationship and a connection number between nodes in a target network includes: obtaining elements in a weighting adjacency matrix W representing the direct connection relation of the target network according to whether each node in the target network is connected with other nodes or not and the quantity relation of the connections; determining the total number of connections between each node and all other nodes in the target network based on the elements of the weighted adjacency matrix to obtain a network degree matrix D G; and determining a probability transition matrix P according to the weighted adjacency matrix W and the network degree matrix D G, wherein the probability transition matrix meets the following expression:
In an embodiment of the present disclosure, determining the number of one-hop object nodes and the number of two-hop object nodes of each node in a target network according to a one-way connection relationship and a two-way connection relationship between each node and the rest of nodes in the target network includes: each node in the target network is used as a current node, and the number of specific nodes with one-way connection relation with the current node is determined as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of the specific nodes with the double-pass connection relation with the current node as the number of the two-hop object nodes of the current node in the rest nodes.
In an embodiment of the present disclosure, the steady state probability of each node of the target network for which the random walk initial node walks satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0,
Wherein r t represents a steady-state probability matrix at time t, and elements in the steady-state probability matrix are steady-state probabilities that random walk initial nodes walk to each node in a target network; lim represents the limit at which time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p 2 denotes the square of the probability transition matrix; the upper corner mark T represents the transposition of the matrix; the upper corner mark-1 outside the bracketing represents the inverse of the matrix; r 0 denotes a random walk initial node vector.
In an embodiment of the present disclosure, determining the relative importance of all nodes of a target network based on the steady state probability of each node of the target network being traversed by a random walk initial node includes: aiming at the ith node in the target network, i is more than or equal to 1 and less than or equal to N, wherein N represents the total number of nodes in the network, and the steady-state probabilities of N random walk initial nodes respectively walking to the ith node are summed to obtain an importance degree score of the ith node; and sequencing all nodes in the target network based on the importance scores of the N nodes in the target network to obtain the relative importance of all nodes in the target network.
In an embodiment of the present disclosure, the target network is a document reference network, and the nodes of the target network are documents; the determining method further comprises the following steps: determining high value documents based on the relative importance of all nodes; or the target network is an internet network, and the nodes of the target network are internet nodes; the determining method further comprises the following steps: determining core Internet nodes based on the relative importance degrees of all the nodes so as to enhance the protection level of the core Internet nodes; or the target network is a traffic network, the nodes of the target network are intersections or cells, streets or roads correspond to the edges between the nodes, and the obstacles encountered by the vehicle running on the edges correspond to the weights of the edges; the determining method further comprises the following steps: and determining the key traffic nodes based on the relative importance degrees of all the nodes so as to enhance the control and evacuation of the key traffic nodes.
A second aspect of the present disclosure provides a network node importance determining apparatus. The above-mentioned determining device includes: the device comprises a probability transition matrix module, a one-jump and two-jump coefficient determining module, a steady-state probability determining module and a relative importance determining module. The probability transition matrix module is used for determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in the target network. The first-hop and second-hop coefficient determining module is configured to determine the number of first-hop object nodes and the number of second-hop object nodes of each node in the target network according to the one-way connection relationship and the two-way connection relationship between each node and the rest nodes in the target network, so as to obtain a proportion value alpha 1、α2 that the number of all the first-hop object nodes and the number of the second-hop object nodes occupy the total number of the object nodes respectively. The total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes. The steady-state probability determining module is used for taking each node in the target network as a random walk initial node, and determining the steady-state probability of the random walk initial node to each node of the target network according to the preset restarting probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix. The relative importance degree determining module is used for determining the relative importance degree of all nodes of the target network according to the steady-state probability of each node of the target network, which is walked by the random walk initial node.
A third aspect of the present disclosure provides an abnormality detection device for biological tissue. The abnormality detection device includes: the system comprises a probability transition matrix module, a first-hop and second-hop coefficient determining module, a steady-state probability determining module, a relative importance determining module and an abnormality determining module. The probability transition matrix module is used for determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in the target network. The target network includes: the neural network of the standard biological tissue and the biological tissue to be detected, the nodes of the target network are neurons, wherein the standard biological tissue is a normal tissue, and the network structure of the neurons of the biological tissue to be detected is the same as the network structure of the neurons of the standard biological tissue. The first-hop and second-hop coefficient determining module is configured to determine the number of first-hop object nodes and the number of second-hop object nodes of each node in the target network according to the one-way connection relationship and the two-way connection relationship between each node and the rest nodes in the target network, so as to obtain a proportion value alpha 1、α2 that the number of all the first-hop object nodes and the number of the second-hop object nodes occupy the total number of the object nodes respectively. The total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes. The steady-state probability determining module is used for taking each node in the target network as a random walk initial node, and determining the steady-state probability of the random walk initial node to each node of the target network according to the preset restarting probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix. The relative importance degree determining module is used for determining the relative importance degree of all nodes of the target network according to the steady-state probability of each node of the target network, which is walked by the random walk initial node. The abnormality determination module is used for determining whether the biological tissue to be detected has abnormality according to whether the relative importance degrees of all nodes of the neural network of the standard biological tissue are consistent with the relative importance degrees of the corresponding nodes of the neural network of the biological tissue to be detected.
A fourth aspect of the present disclosure provides an electronic device. The electronic device includes: one or more processors; and a storage device for storing one or more programs. Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the methods of determining as described above.
A fifth aspect of the present disclosure provides a computer-readable storage medium. The computer readable storage medium has stored thereon executable instructions that, when executed by a processor, cause the processor to implement any of the determination methods described above.
(III) beneficial effects
From the above technical solution, it can be seen that the method, device, electronic equipment and medium for determining the importance of a network node provided by the present disclosure have the following beneficial effects:
Compared with the traditional restarting random walk process, the method provided by the disclosure not only considers the one-hop object node of each node, but also considers the situation of the two-hop object node, and determines the steady probability of the random walk initial node walking to each node of the target network according to the preset restart probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix, thereby realizing the jump process of the current node between the one-hop object nodes and between the two-hop object nodes, avoiding the problem of inaccurate prediction caused by incomplete network structure due to edge deficiency.
Drawings
Fig. 1 is a flow chart of a method of determining importance of a network node according to an embodiment of the present disclosure.
Fig. 2 is a detailed implementation flowchart of operation S11 shown according to an embodiment of the present disclosure.
Fig. 3 is a schematic structural diagram of (a) a target network, (b) a probability transition matrix of the target network, (c) a number of one-hop object nodes and one-hop object nodes of each node in the target network, and (d) an example of a number of two-hop object nodes and two-hop object nodes of each node in the target network according to an embodiment of the disclosure.
Fig. 4 is a flowchart of a method for determining importance of a network node in an application scenario according to an embodiment of the present disclosure.
Fig. 5 is a flowchart of a method for determining importance of a network node in another application scenario according to an embodiment of the present disclosure.
Fig. 6 is a flowchart of a method for determining importance of a network node in yet another application scenario according to an embodiment of the present disclosure.
Fig. 7 is a block diagram of a network node importance determination apparatus according to an embodiment of the present disclosure.
Fig. 8 is a block diagram of a structure of an abnormality detection device of biological tissue according to an embodiment of the present disclosure.
Detailed Description
The embodiment of the disclosure provides a method, a device, electronic equipment and a medium for determining the importance of network nodes, which not only consider the first-hop object node of each node, but also consider the situation of the second-hop object node, determine the steady-state probability of the random walk initial node walking to each node of a target network according to preset restart probability, random walk initial node vector, proportion value alpha 1, proportion value alpha 2 and probability transition matrix, realize the jump process of the current node between the first-hop object nodes and between the second-hop object nodes, and can avoid the problem of inaccurate prediction caused by incomplete network structure due to edge deletion.
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
A first exemplary embodiment of the present disclosure provides a method of determining importance of a network node.
Fig. 1 is a flow chart of a method of determining importance of a network node according to an embodiment of the present disclosure.
Referring to fig. 1, the method for determining importance of a network node includes the following operations: s11, S12, S13 and S14.
In operation S11, a probability transition matrix is determined according to the connection relationship and the number of connections between the nodes in the target network.
In operation S12, the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network are determined according to the one-way connection relationship and the two-way connection relationship between each node and the rest of the nodes in the target network, so as to obtain a proportional value α 1、α2 that the number of all the first-hop object nodes and the number of the second-hop object nodes occupy the total number of the object nodes respectively, where the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
In operation S13, each node in the target network is used as a random walk initial node, and a steady probability that the random walk initial node walks to each node in the target network is determined according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2 and a probability transition matrix.
In operation S14, the relative importance of all nodes of the target network is determined according to the steady-state probability of each node of the target network where the random walk initial node walks.
Compared with the traditional restarting random walk process, the node importance determination method based on restarting the two-hop random walk not only considers the one-hop object node of each node, but also considers the situation of the two-hop object node, and determines the steady-state probability of the random walk initial node to each node of the target network according to the preset restarting probability, the random walk initial node vector, the proportional value alpha 1, the proportional value alpha 2 and the probability transition matrix, thereby realizing the jump process of the current node between the one-hop object nodes and between the two-hop object nodes, avoiding the problem of inaccurate prediction caused by incomplete network structure due to edge deficiency, and obtaining a stable numerical value by restarting the two-hop random walk of each node in the target network, wherein the numerical value can represent the importance degree of the node, thereby obtaining the relative importance degree of all nodes.
Fig. 2 is a detailed implementation flowchart of operation S11 shown according to an embodiment of the present disclosure.
In an embodiment of the present disclosure, referring to fig. 2, an operation S11 of determining a probability transition matrix according to a connection relationship and the number of connections between nodes in a target network includes the following sub-operations: s111, S112, and S113.
In sub-operation S111, elements in the weighted adjacency matrix W representing the direct connection relationship of the target network are obtained according to whether each node in the target network is connected with the other nodes and the number relationship of the connections.
In sub-operation S112, the total number of connections between each node and all the other nodes in the target network is determined based on the elements of the weighted adjacency matrix to obtain the degree-of-network matrix D G.
In sub-operation S113, a probability transition matrix P is determined from the weighted adjacency matrix W and the degree of network matrix D G, the probability transition matrix satisfying the following expression:
Fig. 3 is a schematic structural diagram of (a) a target network, (b) a probability transition matrix of the target network, (c) a number of one-hop object nodes and one-hop object nodes of each node in the target network, and (d) an example of a number of two-hop object nodes and two-hop object nodes of each node in the target network according to an embodiment of the disclosure.
To exemplify how the above operations S11 to S14 are performed for a given target network, the following description is made with reference to (a) to (d) in fig. 3.
Referring to fig. 3 (a), the connection relationship between each node in a given target network is determined, and all nodes in the target network are numbered sequentially to facilitate description of each node, where the target network includes 5 node examples, and the 5 nodes are described as node 1, node 2, node 3, node 4, and node 5, respectively.
In sub-operation S111, elements in the weighted adjacency matrix W characterizing the direct connection relationship of the target network are obtained according to whether each node in the target network is connected with the other nodes and the number relationship of the connections.
Illustratively, the target network is denoted as G (V, E, W), where v= { V 1,v2,...,vn } is the set of all nodes in the target network,Is the set of conjoined edges between nodes, |v|=n and |e|=m are the total number of nodes and the total number of conjoined edges in the target network, respectively.
The weighted adjacency matrix W is expressed as follows:
wherein, Representing elements in the weighted adjacency matrix W; the value of i in v i is 1 to the total number of nodes n, the value of j in v j is 1 to the total number of nodes n, and when v i＝vj, the value of i is/(v)
For the target network illustrated in fig. 3 (a), n=5, m=6. For the i-th node, i=1, 2, … …, n, it can be determined whether the i-th node is connected with the rest of the nodesIf the value of (2) is equal to 0, if the ith node is not connected with the jth node, then/>If the ith node has a connection with the jth node, then/>The size of a particular value may represent the number relationship of connections.
For the target network shown in fig. 3 (a), the weighted adjacency matrix W shown in fig. 3 (b) can be obtained according to whether each node is connected with the rest of nodes and the number relationship of the connections. In the weighted adjacency matrix W illustrated in fig. 3 (b), the number of connections between the respective nodes is 1, and thusIn other embodiments, the value of each element is 1, and in other embodiments, the/>, can be obtained according to the number of connections between each node in the case of the connection relationshipIn the case of (2) the values of the elements.
For example, in the present embodiment, the weighting adjacency matrix W obtained is in the following form:
in sub-operation S112, the total number of connections between each node and all the other nodes in the target network is determined based on the elements of the weighted adjacency matrix, and the expression of the network degree matrix D G is obtained as follows:
Based on the above, for a target network containing n nodes, the network degree matrix D G is a diagonal matrix, and the network degree matrix D G may be expressed as follows:
DG＝diag{d1,d2,...,dn} (4)，
Wherein the values of the diagonal elements satisfy the following expression:
in this embodiment, the elements of the same row in the weighted adjacency matrix W are added up, corresponding to the elements of each diagonal in the resulting network degree matrix D G,
DG＝diag{2，2，3，3，2} (6)。
In the present disclosure, a probability transition matrix P is determined from the weighted adjacency matrix W and the networking degree matrix D G, and the probability transition matrix satisfies the following expression:
Substituting equations (6) and (2) of the present embodiment can obtain the probability transition matrix P in the target network of the present embodiment. The probability transition matrix P of the present embodiment satisfies the following expression:
In an embodiment of the present disclosure, determining the number of one-hop object nodes and the number of two-hop object nodes of each node in a target network according to a one-way connection relationship and a two-way connection relationship between each node and the rest of nodes in the target network includes: each node in the target network is used as a current node, and the number of specific nodes with one-way connection relation with the current node is determined as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of the specific nodes with the double-pass connection relation with the current node as the number of the two-hop object nodes of the current node in the rest nodes.
Referring to fig. 3 (a) and (c), the current nodes are node 1, node 2, node 3, node 4 and node 5, respectively, and the specific nodes having a one-way connection relationship with node 1 are: node 2 and node 4. For node 2, the specific nodes with single-way connection relation with node 2 are respectively: node 1 and node 3. For node 3, the specific nodes with single-way connection relation with node 3 are respectively: node 2, node 4 and node 5. For node 4, the specific nodes with single-pass connection relation with node 4 are respectively: node 1, node 3, and node 5. For node 5, the specific nodes with single-pass connection relation with node 5 are respectively: node 3 and node 4. Therefore, the number of the one-hop object nodes with the current node being the node 1 can be determined to be 2, the number of the one-hop object nodes with the current node being the node 2 is determined to be 2, the number of the one-hop object nodes with the current node being the node 3 is determined to be 3, the number of the one-hop object nodes with the current node being the node 4 is determined to be 3, and the number of the one-hop object nodes with the current node being the node 5 is determined to be 2.
Referring to fig. 3 (a) and (d), the current node is node 1, node 2, node 3, node 4, and node 5, and the specific nodes having a two-way connection relationship with node 1 are: node 3 and node 5. For the node 2, specific nodes with a two-way connection relationship with the node 2 are respectively: node 4 and node 5. For node 3, the specific nodes with single-way connection relation with node 3 are respectively: node 1, node 4 and node 5. For node 4, the specific nodes with single-pass connection relation with node 4 are respectively: node 2, node 3 and node 5. For node 5, the specific nodes with single-pass connection relation with node 5 are respectively: node 1, node 2, node 3 and node 4. Therefore, the number of the two-hop object nodes with the current node being the node 1 can be determined to be 2, the number of the two-hop object nodes with the current node being the node 2 is determined to be 2, the number of the two-hop object nodes with the current node being the node 3 is determined to be 3, the number of the two-hop object nodes with the current node being the node 4 is determined to be 3, and the number of the one-hop object nodes with the current node being the node 5 is determined to be 4.
Thus, the total object node number is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes. In this embodiment, the total number of object nodes is: (2+2+3+3+2) + (2+2+3+3+4) =12+14=26.
The proportion value alpha 1、α2 of the number of all the first-hop object nodes and the number of the second-hop object nodes occupying the total number of the object nodes is respectively as follows:
α1＝2+2+3+3+2/(12+14)＝12/26 (9)，
α2＝2+2+3+3+4/(12+14)＝14/26 (10)。
In operation S13, each node in the target network is used as a random walk initial node, and a steady probability that the random walk initial node walks to each node in the target network is determined according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2 and a probability transition matrix. The steady state probability of each node of the random walk initial node to the target network satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0 (11),
Wherein r t represents a steady-state probability matrix at time t, and elements in the steady-state probability matrix are steady-state probabilities that random walk initial nodes walk to each node in a target network; lim represents the limit at which time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p 2 denotes the square of the probability transition matrix; the upper corner mark T represents the transposition of the matrix; the upper corner mark-1 outside the bracketing represents the inverse of the matrix; r 0 denotes a random walk initial node vector.
Illustratively, the random walk initial node vector may be any one of nodes 1 to 5, taking node 1 as an example, when the random walk initial node is node 1, the random walk initial node vector is expressed as:
r0[1,0,0,0,0]T (12)。
Wherein, the T in the upper right corner represents the transpose, and r 0 is the column vector.
Similarly, when the ith node is used as the initial node of the random walk, the position corresponding to the ith node has a value of 1, and the other positions are all 0. For example, when node 2 is the initial node for random walk, r 0＝(0,1,0,0,0)T.
In this embodiment, the preset value of c is set to 0.85, that is, the preset restart probability is set to 0.15, and then the preset restart probability, the random walk initial node vector r 0 in the formula (12) of this embodiment, the proportional value α 1 in the formula (9), the proportional value α 2 in the formula (10), and the probability transition matrix P in the formula (8) of this embodiment are substituted into the formula (11), so that the steady probability of each node in the nodes 1 to 5 of the target network when the random walk initial node is the node 1 can be obtained, as shown in the following formula (13):
based on the above, the nodes 2 to 5 can be used as the random walk initial nodes, respectively, and the steady-state probability is calculated based on the formula (13), whereby the steady-state probability matrix in which the nodes 1 to 5 are used as the random walk initial nodes, respectively, can be obtained:
Π＝{πij}|V|×|V| (14)，
Where pi ij represents the steady state probability of the random walk initial node v i to walk to node v j.
In this embodiment, the values of the elements in the obtained steady-state probability matrix are represented by the following formula (15):
In an embodiment of the present disclosure, the operation S14 of determining the relative importance of all nodes of the target network according to the steady-state probability of each node of the target network where the random walk initial node walks includes the following sub-operations: s141 and S142.
In operation S141, for the ith node in the target network, 1 is greater than or equal to i and less than or equal to N, where N represents the total number of nodes in the network, the steady probabilities of the N random walk initial nodes respectively walking to the ith node are summed to obtain an importance score of the ith node.
The importance score S 2-hop(vi) of the i-th node v i satisfies the following expression:
substituting equation (15) into equation (16) according to this embodiment can calculate the importance scores S 2-hop for nodes 1 to 5 in the network. Aiming at the node 1, adding all elements in a column of a steady-state probability matrix II to obtain the importance scores of N random walk initial nodes to the 1 st node, and obtaining the importance scores of the nodes 2 to 5 in the same way to obtain the importance scores of all the nodes: s 2-hop＝[0.8610,0.8610,1.2130,1.2130,0.8520]T.
In operation S142, all nodes in the target network are ranked based on the magnitudes of the importance scores of the N nodes in the target network to obtain the relative importance levels of all nodes of the target network.
Fig. 4 is a flowchart of a method for determining importance of a network node in an application scenario according to an embodiment of the present disclosure.
In a scenario, documents such as papers, journals or patents generally need to be cited each other, but the cited relation between the documents is complex, the cited relation networks are interwoven each other, and how to mine documents with high value in the complex document cited scenario is a great technical difficulty.
The above determination method is applied to solving the practical problem cited in this document in this embodiment, and the determination method of the importance of the network node in this embodiment includes the following operations: s11a, S12a, S13a, S14a and S15a.
In operation S11a, a probability transition matrix is determined according to the connection relationship and the number of connections between each node in the target network. In this embodiment, the target network is a document reference network, and the nodes are document nodes.
In operation S12a, the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network are determined according to the one-way connection relationship and the two-way connection relationship between each node and the rest of the nodes in the target network, so as to obtain a proportional value α 1、α2 of the total number of the first-hop object nodes and the second-hop object nodes, where the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
In operation S13a, each node in the target network is used as a random walk initial node, and a steady-state probability that the random walk initial node walks to each node in the target network is determined according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2 and a probability transition matrix.
In operation S14a, the relative importance of all nodes of the target network is determined according to the steady-state probability of each node of the target network where the random walk initial node walks.
In operation S15a, high value documents are determined based on the relative importance levels of all nodes.
The above operations S11a to S14a are performed as described above with reference to the operations S11 to S14. In operation S15a, for example, a document node having a importance score greater than a set score in the document referencing network may be determined as a high-value document. Document nodes with top 10%, 20%, or other set proportions of importance in the document referencing network may also be determined to be high value documents. Operation S15a in the present disclosure determines the high-value document based on the association of the relative importance degrees of all nodes with the high-value document, and the determination method may be other ways, not limited to the above-listed embodiments.
Fig. 5 is a flowchart of a method for determining importance of a network node in another application scenario according to an embodiment of the present disclosure.
In another scenario, the power optical fiber network is used as a carrier network for voice, data, video, power grid operation and other different services, and the reliability and the safety of the power optical fiber network are important research subjects. The node is one of the core elements of the network, the importance of the node in the network is different, the importance of the core nodes of the network is higher, if the core nodes are destroyed, the reliability of the network is reduced, and even large-area communication is interrupted. Therefore, the method has important practical value in evaluating the importance of the nodes in the power optical fiber network and exploring the important nodes in the network. On one hand, the reliability and the survivability of the whole network can be improved by protecting the core node with emphasis; on the other hand, in the aspect of network survivability, the protection level of the nodes can be determined according to the importance degree of the nodes, so that the information transmission reliability of power grid operation and management is ensured.
In this embodiment, the above determination method is applied to solving the actual problem of network security, and the determination method of the importance of the network node in this embodiment includes the following operations: s11b, S12b, S13b, S14b and S15b.
In operation S11b, a probability transition matrix is determined according to the connection relationship and the number of connections between each node in the target network. In this embodiment, the target network is an internet network, and the node is an internet node.
In operation S12b, the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network are determined according to the one-way connection relationship and the two-way connection relationship between each node and the rest of the nodes in the target network, so as to obtain a proportional value α 1、α2 of the total number of the first-hop object nodes and the second-hop object nodes, where the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
In operation S13b, each node in the target network is used as a random walk initial node, and a steady-state probability that the random walk initial node walks to each node in the target network is determined according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2 and a probability transition matrix.
In operation S14b, the relative importance of all nodes of the target network is determined according to the steady-state probability of each node of the target network where the random walk initial node walks.
In operation S15b, the core internet node is determined based on the relative importance levels of all the nodes to enhance the protection level of the core internet node.
The above operations S11b to S14b are performed as described above with reference to the operations S11 to S14. In operation S15b, for example, an internet node having a importance score greater than a set score in the internet network may be determined as a core internet node. The top 10%, 20%, or other set proportion of the importance level ranking internet nodes in the internet network may also be determined to be core internet nodes. Operation S15b in the present disclosure determines the core internet node based on the association of the relative importance degrees of all the nodes with the core internet node, and the determination method may be other ways, not limited to the above-listed embodiments.
Fig. 6 is a flowchart of a method for determining importance of a network node in yet another application scenario according to an embodiment of the present disclosure.
In yet another scenario, the selection of the intersection nodes of the urban complex traffic network has important significance for the structural stability and control effectiveness of the traffic network. In an urban traffic network, the randomness attack may be a traffic accident, a failure of an intersection traffic light, traffic control and the like, and when a key node of the urban traffic complex network is subjected to selective attack, the urban traffic network is in danger of breakdown. The evaluation and selection of the network key nodes are of great significance to the implementation of the regional traffic signal control system.
In this embodiment, the above determination method is applied to solving the actual problem of traffic guiding control, and the determination method of the importance of the network node in this embodiment includes the following operations: s11c, S12c, S13c, S14c and S15c.
In operation S11c, a probability transition matrix is determined according to the connection relationship and the number of connections between the nodes in the target network. In this embodiment, the target network is a traffic network, the nodes of the target network are intersections or cells, the streets or roads correspond to the edges between the nodes, and the obstacles encountered by the vehicle traveling on the edges correspond to the weights of the edges.
In operation S12c, the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network are determined according to the one-way connection relationship and the two-way connection relationship between each node and the rest of the nodes in the target network, so as to obtain a proportional value α 1、α2 of the total number of the first-hop object nodes and the second-hop object nodes, where the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
In operation S13c, each node in the target network is used as a random walk initial node, and a steady-state probability that the random walk initial node walks to each node in the target network is determined according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2 and a probability transition matrix.
In operation S14c, the relative importance of all nodes of the target network is determined according to the steady-state probability of each node of the target network where the random walk initial node walks.
In operation S15c, critical traffic nodes are determined based on the relative importance levels of all nodes to enhance control and evacuation of the critical traffic nodes.
The above operations S11c to S14c are performed as described above with reference to the operations S11 to S14. In operation S15c, for example, an intersection or a cell node in the traffic network having a importance score greater than the set score may be determined as a key traffic node. The top 10%, 20%, or other scaled intersections or cell nodes in the traffic network may also be determined to be critical traffic nodes. Operation S15c in the present disclosure determines the critical traffic node based on the correlation of the relative importance degrees of all nodes with the critical traffic node, and the determination method may be other ways, not limited to the above-listed embodiments.
Based on the same technical idea, a second exemplary embodiment of the present disclosure provides a network node importance determining apparatus.
Fig. 7 is a block diagram of a network node importance determination apparatus according to an embodiment of the present disclosure.
Referring to fig. 7, the determination device 2 includes: a probability transition matrix module 21, a one-and two-hop coefficient determination module 22, a steady-state probability determination module 23, and a relative importance determination module 24.
The probability transition matrix module 21 is configured to determine a probability transition matrix according to the connection relationship and the number of connections between the nodes in the target network.
The one-hop and two-hop coefficient determining module 22 is configured to determine the number of one-hop object nodes and the number of two-hop object nodes of each node in the target network according to the one-way connection relationship and the two-way connection relationship between each node and the rest nodes in the target network, so as to obtain a proportion value α 1、α2 of the total number of object nodes occupied by all the number of one-hop object nodes and the number of two-hop object nodes. The total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
The steady-state probability determining module 23 is configured to determine, with each node in the target network as a random walk initial node, a steady-state probability of the random walk initial node walking to each node in the target network according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2, and a probability transition matrix.
The relative importance determination module 24 is configured to determine the relative importance of all nodes of the target network based on the steady-state probability of each node of the target network being traversed by the random walk initial node.
Based on the same technical idea, a third exemplary embodiment of the present disclosure provides an abnormality detection device of biological tissue.
Fig. 8 is a block diagram of a structure of an abnormality detection device of biological tissue according to an embodiment of the present disclosure.
Referring to fig. 8, the abnormality detection device 3 of the present embodiment includes: a probability transition matrix module 31, a one-and two-hop coefficient determination module 32, a steady-state probability determination module 33, a relative importance determination module 34, and an anomaly determination module 35.
The probability transition matrix module 31 is configured to determine a probability transition matrix according to a connection relationship and a connection number between nodes in the target network.
The target network includes: the neural network of the standard biological tissue and the biological tissue to be detected, the nodes of the target network are neurons, wherein the standard biological tissue is a normal tissue, and the network structure of the neurons of the biological tissue to be detected is the same as the network structure of the neurons of the standard biological tissue.
The first-hop and second-hop coefficient determining module 32 is configured to determine the number of first-hop object nodes and the number of second-hop object nodes of each node in the target network according to the one-way connection relationship and the two-way connection relationship between each node and the rest nodes in the target network, so as to obtain a proportion value α 1、α2 of the total number of object nodes occupied by all the first-hop object nodes and the second-hop object nodes respectively. The total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes.
The steady-state probability determining module 33 is configured to determine, with each node in the target network as a random walk initial node, a steady-state probability of the random walk initial node walking to each node in the target network according to a preset restart probability, a random walk initial node vector, a proportion value α 1, a proportion value α 2, and a probability transition matrix.
The relative importance determination module 34 is configured to determine the relative importance of all nodes of the target network based on the steady-state probability of each node of the target network being traversed by the random walk initial node.
The abnormality determination module 35 is configured to determine whether an abnormality exists in the biological tissue to be detected according to whether the relative importance levels of all nodes of the neural network of the standard biological tissue are consistent with the relative importance levels of the corresponding nodes of the neural network of the biological tissue to be detected.
For example, the biological tissue to be detected is a brain tissue of the subject to be detected, the standard biological tissue is a normal brain tissue sample, wherein the brain tissue sample is identical to the network structure of the neurons of the brain tissue of the subject to be detected, that is, the structure of the target network is identical, if the internal structure or the internal functional unit of one of the neurons is abnormal, when the relative importance degree of each node in the brain tissue sample and the relative importance degree of the corresponding node in the brain tissue of the subject to be detected are compared based on the abnormality determination module 35, if the relative importance degrees of all the nodes of the neural network of the standard biological tissue are not identical to the relative importance degrees of the corresponding nodes of the neural network of the biological tissue to be detected, it may be determined that the brain tissue of the subject to be detected is abnormal.
The abnormality detection device for biological tissue of the present embodiment can be used for detecting Alzheimer's disease.
Any number of modules, sub-modules, units, sub-units, or at least some of the functionality of any number of the sub-units according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented as split into multiple modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in any other reasonable manner of hardware or firmware that integrates or encapsulates the circuit, or in any one of or a suitable combination of three of software, hardware, and firmware. Or one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be at least partially implemented as computer program modules, which, when executed, may perform the corresponding functions.
For example, any of the probability transition matrix module 21, the one-and two-hop coefficient determination module 22, the steady-state probability determination module 23, and the relative importance determination module 24 may be incorporated in one module to be implemented, or any one of the modules may be split into a plurality of modules. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. According to embodiments of the present disclosure, at least one of the probability transition matrix module 21, the one-and two-hop coefficient determination module 22, the steady-state probability determination module 23, and the relative importance determination module 24 may be implemented, at least in part, as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or as hardware or firmware in any other reasonable manner of integrating or packaging the circuit, or as any one of or a suitable combination of any of the three. Or at least one of the probability transition matrix module 21, the one-and two-hop coefficient determination module 22, the steady-state probability determination module 23 and the relative importance determination module 24 may be at least partially implemented as computer program modules which, when run, may perform the respective functions.
A fourth exemplary embodiment of the present disclosure provides an electronic device. The electronic device includes: one or more processors; and a storage device for storing one or more programs. Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement any of the methods of determining as described above.
The method flow according to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by a processor. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
A fifth exemplary embodiment of the present disclosure provides a computer-readable storage medium. The computer readable storage medium has stored thereon executable instructions that, when executed by a processor, cause the processor to implement any of the determination methods described above.
The computer-readable storage medium may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The use of ordinal numbers such as "first," "second," "third," etc., in the description and the claims to modify a corresponding element does not by itself connote any ordinal number of elements or the order of manufacturing or use of the ordinal numbers in a particular claim, merely for enabling an element having a particular name to be clearly distinguished from another element having the same name.
Furthermore, the word "comprising" or "comprises" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
While the foregoing embodiments have been described in some detail for purposes of clarity of understanding, it will be understood that the foregoing embodiments are merely illustrative of the invention and are not intended to limit the invention, and that any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (9)

1. A method for determining importance of a network node, comprising:
Determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in the target network;
Determining the number of one-hop object nodes and the number of two-hop object nodes of each node in a target network according to the one-way connection relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain a proportion value alpha 1、α2 of the number of all the one-hop object nodes and the number of the two-hop object nodes occupying the total number of the object nodes respectively, wherein the total number of the object nodes is equal to the sum of the number of the one-hop object nodes and the number of the two-hop object nodes of all the nodes;
Each node in the target network is used as a random walk initial node, and the steady-state probability of the random walk initial node to each node of the target network is determined according to the preset restarting probability, the random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix; and
Determining the relative importance of all nodes of the target network based on the steady state probability of each node of the target network being travelled by the random walk-initiating node,
The target network is a document reference network, and nodes of the target network are documents; the determining method further comprises the following steps: determining high value documents based on the relative importance of all nodes; or alternatively
The target network is an internet network, and the nodes of the target network are internet nodes; the determining method further comprises the following steps: determining core Internet nodes based on the relative importance degrees of all the nodes so as to enhance the protection level of the core Internet nodes; or alternatively
The target network is a traffic network, nodes of the target network are intersections or cells, streets or roads correspond to edges between the nodes, and obstacles encountered by vehicles running on the edges correspond to the weights of the edges; the determining method further comprises the following steps: and determining the key traffic nodes based on the relative importance degrees of all the nodes so as to enhance the control and evacuation of the key traffic nodes.
2. The method according to claim 1, wherein determining the probability transition matrix according to the connection relation and the number of connections between the nodes in the target network comprises:
Obtaining elements in a weighting adjacency matrix W representing a direct connection relation of a target network according to whether each node in the target network is connected with other nodes or not and the quantity relation of the connections;
Determining the total number of connections between each node and all other nodes in the target network based on the elements of the weighted adjacency matrix to obtain a network degree matrix D G; and
Determining a probability transition matrix P according to the weighted adjacency matrix W and the networking degree matrix D G, wherein the probability transition matrix meets the following expression:
3. the method according to claim 1, wherein determining the number of the first-hop object nodes and the number of the second-hop object nodes of each node in the target network according to the one-way connection relationship and the two-way connection relationship between each node and the rest of the nodes in the target network comprises:
Each node in the target network is used as a current node, and the number of specific nodes with one-way connection relation with the current node is determined as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of the specific nodes with the double-pass connection relation with the current node as the number of the two-hop object nodes of the current node in the rest nodes.
4. The determination method according to claim 1, wherein the steady-state probability of each node of the random walk initial node walk to the target network satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0,
Wherein r t represents a steady-state probability matrix at time t, and elements in the steady-state probability matrix are steady-state probabilities that random walk initial nodes walk to each node in a target network; lim represents the limit at which time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p 2 denotes the square of the probability transition matrix; the upper corner mark T represents the transposition of the matrix; the upper corner mark-1 outside the bracketing represents the inverse of the matrix; r 0 denotes a random walk initial node vector.
5. The method according to claim 1, wherein determining the relative importance of all nodes of the target network based on the steady-state probability of each node of the target network where the random walk initial node walks, comprises:
Aiming at the ith node in the target network, i is more than or equal to 1 and less than or equal to N, wherein N represents the total number of nodes in the network, and the steady-state probabilities of N random walk initial nodes respectively walking to the ith node are summed to obtain an importance degree score of the ith node; and
And sequencing all nodes in the target network based on the importance scores of the N nodes in the target network to obtain the relative importance of all the nodes in the target network.
6. A device for determining importance of a network node, comprising:
The probability transition matrix module is used for determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in the target network;
The first-hop and second-hop coefficient determining module is used for determining the number of first-hop object nodes and the number of second-hop object nodes of each node in the target network according to the one-way connection relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain a proportion value alpha 1、α2 of the number of all the first-hop object nodes and the number of the second-hop object nodes to the total number of the object nodes, wherein the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes;
The steady-state probability determining module is used for taking each node in the target network as a random walk initial node, and determining the steady-state probability of the random walk initial node to each node of the target network according to a preset restarting probability, a random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix; and
A relative importance degree determining module for determining the relative importance degree of all nodes of the target network according to the steady state probability of each node of the target network where the random walk initial node walks,
The target network is a document reference network, and nodes of the target network are documents; the determining means is further for: determining high value documents based on the relative importance of all nodes; or alternatively
The target network is an internet network, and the nodes of the target network are internet nodes; the determining means is further for: determining core Internet nodes based on the relative importance degrees of all the nodes so as to enhance the protection level of the core Internet nodes; or alternatively
The target network is a traffic network, nodes of the target network are intersections or cells, streets or roads correspond to edges between the nodes, and obstacles encountered by vehicles running on the edges correspond to the weights of the edges; the determining means is further for: and determining the key traffic nodes based on the relative importance degrees of all the nodes so as to enhance the control and evacuation of the key traffic nodes.
7. An abnormality detection device for biological tissue, comprising:
The probability transition matrix module is used for determining a probability transition matrix according to the connection relation and the connection quantity among all nodes in a target network, and the target network comprises: the neural network of the standard biological tissue and the biological tissue to be detected, wherein the nodes of the target network are neurons, the standard biological tissue is a normal tissue, and the network structure of the neurons of the biological tissue to be detected is the same as the network structure of the neurons of the standard biological tissue;
The first-hop and second-hop coefficient determining module is used for determining the number of first-hop object nodes and the number of second-hop object nodes of each node in the target network according to the one-way connection relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain a proportion value alpha 1、α2 of the number of all the first-hop object nodes and the number of the second-hop object nodes to the total number of the object nodes, wherein the total number of the object nodes is equal to the sum of the number of the first-hop object nodes and the number of the second-hop object nodes of all the nodes;
The steady-state probability determining module is used for taking each node in the target network as a random walk initial node, and determining the steady-state probability of the random walk initial node to each node of the target network according to a preset restarting probability, a random walk initial node vector, the proportion value alpha 1, the proportion value alpha 2 and the probability transition matrix;
the relative importance degree determining module is used for determining the relative importance degree of all nodes of the target network according to the steady-state probability of each node of the target network, which is caused by the random walk initial node to walk; and
The abnormality determining module is used for determining whether the biological tissue to be detected has abnormality according to whether the relative importance degrees of all nodes of the neural network of the standard biological tissue are consistent with the relative importance degrees of the corresponding nodes of the neural network of the biological tissue to be detected.
8. An electronic device, comprising:
One or more processors; and
A storage means for storing one or more programs;
Wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of determining of any of claims 1-5.
9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to implement the determining method of any of claims 1-5.
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