CN112364295A - Method and device for determining importance of network node, electronic equipment and medium - Google Patents
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Abstract
In the method, a probability transition matrix is determined according to the connection relation and the connection quantity between nodes in a target network. Determining the number of one-hop object nodes and the number of two-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 the proportional value alpha of the total object node number occupied by the number of all the one-hop object nodes and the number of the two-hop object nodes respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes. According to the preset restart probability, the random walk initial node vector and the proportional value alpha1Proportional value alpha2And determining the random walk initial node to walk to each target network by the probability transition matrixThe steady state probability of a node. And determining the relative importance degree of all the nodes according to the steady-state probability.
Description
Technical Field
The present disclosure relates to a method, an apparatus, an electronic device, and a medium for determining importance of a network node, and in particular, to a method, a system, an electronic device, and a medium for determining importance of a network node based on a random walk with two hops restart.
Background
The network is a data structure capable of storing the interrelationship between entities, each entity is used as a node of the network, and the information, data or signal interaction relationship between the entities is constructed as the network. A complex network is a network structure formed by a huge number of nodes and intricate relationships between the nodes.
There are various types of complex networks in the real world, including: social networks, such as friend circle networks, related enterprise networks, and the like, information networks, such as the world wide web, communication networks, and the like, and biological networks, such as brain networks, protein networks, and disease networks, and the like.
Determining the importance of the nodes in the networks helps to deeply understand the networks for wide applications in the internet field and data mining field, such as information processing and prediction by using the importance of each node.
The inventor finds the following problems in the existing application scenarios in the process of implementing the present disclosure: most of the existing network node importance measurement methods depend on the integrity of a network structure, and if the connection edges in the network are missing, the prediction method has certain limitations, and the prediction result is not accurate enough.
For example, in an application scenario, documents such as articles, periodicals, or patents and the like generally need to be cited to each other, the citation relations among the documents are complex, the citation relation networks are mutually interlaced, and how to dig out documents with high values in the complex document citation scenario is a great technical difficulty.
In another scenario, the power fiber network is used as a carrier network for various services such as voice, data, video, power grid operation, and the like, and the reliability and security thereof are an important research topic. The node is one of core elements of the network, the importance of the node in the network is different, the importance of the core node of the network is higher, and if the core node is damaged, the reliability of the network is reduced, and even large-area communication interruption is caused. Therefore, it is of great practical value to evaluate the importance of nodes in the power optical fiber network and to discover important nodes in the network. On one hand, the reliability and the survivability of the whole network can be improved by mainly protecting the core nodes; on the other hand, in the aspect of network survivability, the protection levels of the nodes can be determined according to the importance degree of the nodes, and further the information transmission reliability of the operation and management of the power grid is ensured.
In another scene, the selection of the intersection nodes of the urban complex traffic network has important significance on the structural stability and the control effectiveness of the traffic network. In the urban traffic network, the random attack may be a traffic accident, an intersection traffic light failure, traffic control and the like, and when a key node of the urban traffic complex network is selectively attacked, 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 each of the above scenarios, the current method for measuring/determining the importance of network nodes mostly depends on the integrity of the network structure, and if there is a missing situation on the connection edges in the network, the prediction method has certain limitations, resulting in inaccurate prediction results.
Disclosure of Invention
Technical problem to be solved
The present disclosure provides a method, an apparatus, an electronic device, and a medium for determining importance of a network node, so as to at least partially solve the above-mentioned technical problems.
(II) technical scheme
A first aspect of the present disclosure provides a method for determining importance of a network node. The determination method comprises the following steps: and determining the probability transfer matrix according to the connection relation and the connection quantity among all nodes in the target network. The above determination method further includes: determining 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 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 of the total number of object nodes occupied by the number of the one-hop object nodes and the number of the two-hop object nodes respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes.The above determination method further includes: taking each node in the target network as a random walk initial node, and performing restart according to preset restart probability, a random walk initial node vector and a proportional value alpha1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network. The above determination method further includes: and determining the relative importance degree of all nodes of the target network according to the steady-state probability of each node which is walked to the target network by the random walk initial node.
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 weighted 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 and the connection quantity relation; determining the total number of connections between each node and all the other nodes in the target network based on the elements of the weighted adjacency matrix to obtain a network degree matrix DG(ii) a And according to the weighted adjacent matrix W and the network degree matrix DGDetermining a probability transition matrix P, the probability transition matrix satisfying the following expression:
in the 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 the target network according to the one-way connection relationship and the two-way connection relationship between each node and the other nodes in the target network includes: taking each node in the target network as a current node, and determining the number of specific nodes having one-way connection relation with the current node as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of specific nodes having a two-way connection relation with the current node in the other nodes as the number of two-hop object nodes of the current node.
In an embodiment of the present disclosure, the steady-state probability of the random walk initial node walking to each node of the target network satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0，
wherein r istRepresenting a steady-state probability matrix at the time t, wherein elements in the steady-state probability matrix are the steady-state probability of each node in the target network from the random walk initial node; lim represents the limit at which the time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p2Representing the square of a probability transition matrix; the superscript T represents the transpose of the matrix; the upper superscript-1 outside the bracketing represents the inverse of the matrix; r is0Representing a random walk initial node vector.
In an embodiment of the present disclosure, determining the relative importance of all nodes of the target network according to the steady-state probability of the random walk initial node walking to each node of the target network 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, 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 added to obtain the importance degree score of the ith node; and sequencing all the nodes in the target network based on the importance degree scores of the N nodes in the target network to obtain the relative importance degrees of all the nodes in the target network.
In an embodiment of the present disclosure, the target network is a document citation network, and the nodes of the target network are documents; the determination method further comprises: determining a high-value document based on the relative importance degree of all nodes; or the target network is an internet network, and the nodes of the target network are internet nodes; the determination method further comprises: determining core internet nodes based on the relative importance of all nodes 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 connecting edges between the nodes, and obstacles encountered by the vehicles running on the connecting edges correspond to the weight of the connecting edges; the determination method further comprises: key traffic nodes are determined based on the relative importance of all nodes to enhance control and evacuation of the key traffic nodes.
A second aspect of the present disclosure provides an apparatus for determining importance of a network node. The above-mentioned determining means includes: the device comprises a probability transition matrix module, a one-hop and two-hop coefficient determination module, a steady-state probability determination module and a relative importance degree determination module. And the probability transition matrix module is used for determining the probability transition matrix according to the connection relation and the connection quantity between the 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 the proportional value alpha of the total object node number occupied by the number of all the first-hop object nodes and the number of the second-hop object nodes respectively1、α2. The total number of the object nodes is equal to the sum of the number of the object nodes of one hop and the number of the object nodes of two hops of all the nodes. The steady-state probability determination module is used for taking each node in the target network as a random walk initial node and determining a restart probability, a random walk initial node vector and a proportional value alpha according to preset restart probability1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network. 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 which is walked to the target network 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 device comprises a probability transition matrix module, a one-hop and two-hop coefficient determining module, a steady-state probability determining module, a relative importance degree determining module and an abnormity determining module. And the probability transition matrix module is used for determining the probability transition matrix according to the connection relation and the connection quantity between the 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 node of the target network is a neuron, wherein the standard biological tissue is a normal tissue, and the network structure of the neuron of the biological tissue to be detected is the same as that of the neuron of the standard biological tissue. One-hop and two-hop coefficient determinationThe fixed module is used for determining 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 relation and the two-way connection relation between each node and the rest nodes in the target network so as to obtain the proportional value alpha of the total number of object nodes occupied by the number of the one-hop object nodes and the number of the two-hop object nodes respectively1、α2. The total number of the object nodes is equal to the sum of the number of the object nodes of one hop and the number of the object nodes of two hops of all the nodes. The steady-state probability determination module is used for taking each node in the target network as a random walk initial node and determining a restart probability, a random walk initial node vector and a proportional value alpha according to preset restart probability1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network. 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 which is walked to the target network by the random walk initial node. 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 the 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 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 any of the determination methods described above.
A fifth aspect of the present disclosure provides a computer-readable storage medium. The above-described 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) advantageous effects
It can be seen from the foregoing technical solutions that the method, apparatus, electronic device, and medium for determining 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 considers not only the one-hop object node of each node, but also the condition of the two-hop object node, and according to the preset restart probability, the random walk initial node vector and the proportional value alpha1Proportional value alpha2And determining the steady-state probability of each node of the random walk initial node to the target network by the probability transfer matrix, realizing the jump process of the current node between one-jump object nodes and between two-jump object nodes, and avoiding the problem of inaccurate prediction caused by incomplete network structure due to edge deletion.
Drawings
Fig. 1 is a flowchart of a method for determining importance of a network node according to an embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a detailed implementation of operation S11 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) the numbers of one-hop object nodes and one-hop object nodes of each node in the target network, and (d) an example of the numbers of two-hop object nodes and two-hop object nodes of each node in the target network, according to the embodiment of the present 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 illustrating 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 illustrating a structure of a device for determining importance of a network node according to an embodiment of the present disclosure.
Fig. 8 is a block diagram illustrating a structure of an abnormality detection apparatus for biological tissue according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a medium for determining importance of a network node, which consider not only a one-hop object node of each node but also a two-hop object node, according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transfer matrix determines the steady-state probability of each node of the random walk initial node to the target network, so that the jump process of the current node between one-hop object nodes and between two-hop object nodes is realized, the problem of inaccurate prediction caused by incomplete network structure due to edge deletion can be avoided, the method can be suitable for networks with different scales and structures, and has wide application scenes.
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
A first exemplary embodiment of the present disclosure provides a method for determining importance of a network node.
Fig. 1 is a flowchart of a method for determining importance of a network node according to an embodiment of the present disclosure.
Referring to fig. 1, the method for determining the importance of the 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 one-hop object nodes and the number of two-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 other nodes in the target network, so as to obtain all the number of one-hop object nodes and all the number of two-hop object nodesThe number of object nodes respectively occupies the proportion value alpha of the total number of object nodes1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes.
In operation S13, each node in the target network is used as a random walk initial node according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S14, the relative importance degree of all nodes of the target network is determined according to the steady-state probability of the random walk initial node walking to each node of the target network.
The determining method of this embodiment considers a random process of message transmission among all nodes in a network, and provides a node importance determining method based on restarting two-hop random walk1Proportional value alpha2And determining the steady-state probability of each node of the random walk initial node to the target network by the probability transfer matrix, realizing the jump process of the current node between one-jump object nodes and between two-jump object nodes, and avoiding the problem of inaccurate prediction caused by incomplete network structure due to edge deletion.
Fig. 2 is a flowchart illustrating a detailed implementation of operation S11 according to an embodiment of the present disclosure.
In the embodiment of the present disclosure, referring to fig. 2, the operation S11 of determining the probability transition matrix according to the connection relationship and the number of connections between the nodes in the target network includes the following sub-operations: s111, S112 and S113.
In sub-operation S111, an element in the weighted adjacency matrix W representing the direct connection relationship of the target network is obtained according to whether each node in the target network is connected to the other nodes and the number relationship of the connections.
In sub-operation S112, the total number of connections between each node and all other nodes in the target network is determined based on the elements of the weighted adjacency matrix to obtain a network degree matrix DG。
In a suboperation S113, a neighbor matrix W and a network degree matrix D are weightedGDetermining a probability transition matrix P, 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) the numbers of one-hop object nodes and one-hop object nodes of each node in the target network, and (d) an example of the numbers of two-hop object nodes and two-hop object nodes of each node in the target network, according to the embodiment of the present disclosure.
To illustrate how the above-described operations S11 to S14 are implemented for a given target network, the following description is made in conjunction with (a) to (d) of 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 in sequence to facilitate description of each node, where the target network includes 5 node examples, and the 5 nodes are respectively described as node 1, node 2, node 3, node 4, and node 5.
In sub-operation S111, an element in the weighted adjacency matrix W representing the direct connection relationship of the target network is obtained according to whether each node in the target network is connected to the other nodes and the number relationship of the connections.
Illustratively, the target network is denoted as G (V, E, W), where V ═ V1，v2，...，vnIs the set of all nodes in the target network,
is the set of connecting edges between nodes, | V | ═ n and | E | ═ m are the total number of nodes and the total number of connecting edges in the target network, respectively.
The expression of the weighted adjacency matrix W is as follows:
wherein,
representing elements in the weighted adjacency matrix W; v. ofiWherein the value of i is taken from 1 to the total number of nodes n, vjThe value of the middle j is taken from 1 to the total number n of nodes when v isi＝vjWhen the temperature of the water is higher than the set temperature,
for the target network illustrated in fig. 3 (a), n is 5 and m is 6. For the ith node, i is 1, 2, … …, n, and the ith node can be determined according to whether the ith node is connected with the rest nodes
Is equal to 0, if the ith node is not connected with the jth node, then
If the ith node has connection with the jth node
The magnitude of the specific value may represent the quantitative relationship of the connection.
For the target network shown in (a) in fig. 3, the weighted adjacency matrix W shown in (b) in fig. 3 can be obtained according to whether each node is connected with the rest nodes and the relation of the number of connections. In the weighted adjacency matrix W illustrated in fig. 3 (b), the number of connections between the respective nodes is 1, and thus
The value of each element is 1, and in other embodiments, the number of connections between each node can be obtained according to the existence of the connection relationship
The value of each element in the case of (1).
For example, in the present embodiment, the obtained weighted adjacency matrix W is of the following form:
in sub-operation S112, the total number of connections between each node and all other nodes in the target network is determined based on the elements of the weighted adjacency matrix, and a network degree matrix D is obtainedGThe expression of (a) is as follows:
based on the above, for a target network comprising n nodes, the network degree matrix DGAs diagonal matrix, a network degree matrix DGCan be expressed in the following form:
DG＝diag{d1，d2，...，dn} (4)，
wherein the value of each diagonal element satisfies the following expression:
in this embodiment, the elements in the same row in the weighted adjacent matrix W are added to obtain the network degree matrix DGThe elements of each of the diagonal corners in the series,
DG＝diag{2，2，3，3，2} (6)。
in this disclosure, the rootAccording to the weighted adjacency matrix W and the network degree matrix DGDetermining a probability transition matrix P, the probability transition matrix satisfying the following expression:
substituting into the formulas (6) and (2) of the present embodiment, the probability transition matrix P in the target network of the present embodiment can be obtained. The probability transition matrix P of the present embodiment satisfies the following expression:
in the 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 the target network according to the one-way connection relationship and the two-way connection relationship between each node and the other nodes in the target network includes: taking each node in the target network as a current node, and determining the number of specific nodes having one-way connection relation with the current node as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of specific nodes having a two-way connection relation with the current node in the other nodes as the number of two-hop object nodes of the current node.
Referring to (a) and (c) of fig. 3, taking node 1, node 2, node 3, node 4 and node 5 as current nodes, respectively, for node 1, the specific nodes having a one-way connection relationship with node 1 are: node 2 and node 4. For node 2, the specific nodes having a one-way connection relationship with node 2 are: node 1 and node 3. For the node 3, the specific nodes having a one-way connection relationship with the node 3 are: node 2, node 4 and node 5. For the node 4, the specific nodes having a one-way connection relationship with the node 4 are: node 1, node 3 and node 5. For the node 5, the specific nodes having a one-way connection relationship with the node 5 are: node 3 and node 4. Therefore, it can be determined that the number of one-hop object nodes of which the current node is the node 1 is 2, the number of one-hop object nodes of which the current node is the node 2 is 2, the number of one-hop object nodes of which the current node is the node 3 is 3, the number of one-hop object nodes of which the current node is the node 4 is 3, and the number of one-hop object nodes of which the current node is the node 5 is 2.
Referring to (a) and (d) in fig. 3, taking node 1, node 2, node 3, node 4 and node 5 as current nodes, respectively, for node 1, the specific nodes having a two-way connection relationship with node 1 are: node 3 and node 5. For the node 2, the specific nodes having a two-way connection relationship with the node 2 are respectively: node 4 and node 5. For the node 3, the specific nodes having a one-way connection relationship with the node 3 are: node 1, node 4 and node 5. For the node 4, the specific nodes having a one-way connection relationship with the node 4 are: node 2, node 3 and node 5. For the node 5, the specific nodes having a one-way connection relationship with the node 5 are: node 1, node 2, node 3 and node 4. Therefore, it can be determined that the number of two-hop object nodes with the current node being the node 1 is 2, the number of two-hop object nodes with the current node being the node 2 is 2, the number of two-hop object nodes with the current node being the node 3 is 3, the number of two-hop object nodes with the current node being the node 4 is 3, and the number of one-hop object nodes with the current node being the node 5 is 4.
Therefore, the total object node number can be obtained, and is equal to the sum of the one-hop object node number and the two-hop object node number 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 number of all the one-hop object nodes and the number of the two-hop object nodes respectively occupy the proportional value alpha of the total number of the object nodes1、α2Respectively 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 according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network. The steady-state probability of the random walk initial node walking to each node of the target network satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0 (11)，
wherein r istRepresenting a steady-state probability matrix at the time t, wherein elements in the steady-state probability matrix are the steady-state probability of each node in the target network from the random walk initial node; lim represents the limit at which the time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p2Representing the square of a probability transition matrix; the superscript T represents the transpose of the matrix; the upper superscript-1 outside the bracketing represents the inverse of the matrix; r is0Representing a random walk initial node vector.
For example, the random walk initial node vector may be any one of node 1 to node 5, and taking node 1 as an example, when the random walk initial node is node 1, the random walk initial node vector is represented in the following form:
r0[1，0，0，0，0]T (12)。
wherein, the upper right T represents transpose and r0Is a column vector.
Similarly, when the ith node is used as a random walk initial node, the position corresponding to the node has a value of 1, and other positions are all 0. For example, when node 2 acts as the random walk initiation node, r0＝(0，1，0，0，0)T。
In this embodiment, if the preset value of c is set to 0.85, that is, the preset restart probability is 0.15, the preset restart probability, the random walk initial node vector r in the formula (12) in this embodiment, is set to0The ratio of alpha in the formula (9)1The proportional value α in the formula (10)2And substituting the probability transition matrix P in the formula (8) in the present embodiment into the formula (11), a random matrix can be obtainedThe steady-state probability of each of the nodes 1 to 5 in the target network when the machine-walk initial node is the node 1 is shown in the following formula (13):
based on the above, the steady-state probability can be calculated based on the formula (13) with the nodes 2 to 5 as the random walk initial nodes, respectively, and thereby the steady-state probability matrix with the nodes 1 to 5 as the random walk initial nodes can be obtained:
Π＝{πij}|V|×|V| (14)，
wherein piijRepresenting a random walk initial node viWalk to node vjThe steady state probability of (c).
In this embodiment, the value of each element in the obtained steady-state probability matrix is shown in the following formula (15):
in an embodiment of the present disclosure, the operation S14 of determining the relative importance degree of all nodes of the target network according to the steady-state probability of the random walk initial node walking to each node of the target network includes the following sub-operations: s141 and S142.
In operation S141, for the ith node in the target network, i is greater than or equal to 1 and less than or equal to N, where N represents the total number of nodes in the network, the steady-state probabilities of N random walk initial nodes respectively walking to the ith node are summed to obtain the importance score of the ith node.
Ith node viS importance score of2-hop(vi) The following expression is satisfied:
substitution into equation (15) according to the present embodimentThe importance scores S of the nodes 1 to 5 in the network can be calculated by the formula (16)2-hop. For the node 1, adding all elements in a column in a steady-state probability matrix pi to obtain importance scores of N random walk initial nodes walking to the 1 st node, obtaining importance scores of the nodes 2 to 5 according to the same mode, and obtaining the importance scores of all the nodes: s2-hop＝[0.8610，0.8610，1.2130，1.2130，0.8520]T。
In operation S142, all nodes in the target network are sorted based on the magnitude of the importance scores of the N nodes in the target network to obtain the relative importance degrees 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 scene, documents such as articles, periodicals or patents are generally cited to each other, the citation relations among the documents are complex, the citation relation networks are mutually interwoven, and how to dig out documents with high values in the complex document citation scene is a great technical difficulty.
The above determination method is applied to solve the practical problem cited in the document in the present embodiment, and the determination method of the importance of the network node in the present embodiment includes the following operations: s11a, S12a, S13a, S14a and S15 a.
In operation S11a, 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 document citation network, and the nodes are document nodes.
In operation S12a, the number of one-hop object nodes and the number of two-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 other nodes in the target network, so as to obtain a ratio α of the number of all one-hop object nodes and the number of two-hop object nodes occupying the total number of object nodes respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes.
In operation S13a, each node in the target network is used as a random walk initial node according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S14a, the relative importance degree of all nodes of the target network is determined according to the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S15a, a high value document is determined based on the relative importance of all nodes.
The above-described operations S11 a-S14 a may be performed as described above with reference to operations S11-S14. In operation S15a, for example, a document node in the document citation network whose importance degree score is greater than a set score may be determined as a high-value document. Document nodes with the importance degree ranked 10%, 20% or other set proportion in the document citation network can also be determined as high-value documents. Operation S15a in the present disclosure determines the high-value document based on the correlation between the relative importance degrees of all the nodes and the high-value document, and the determination method may be other ways, and is 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 fiber network is used as a carrier network for various services such as voice, data, video, power grid operation, and the like, and the reliability and security thereof are an important research topic. The node is one of core elements of the network, the importance of the node in the network is different, the importance of the core node of the network is higher, and if the core node is damaged, the reliability of the network is reduced, and even large-area communication interruption is caused. Therefore, it is of great practical value to evaluate the importance of nodes in the power optical fiber network and to discover important nodes in the network. On one hand, the reliability and the survivability of the whole network can be improved by mainly protecting the core nodes; on the other hand, in the aspect of network survivability, the protection levels of the nodes can be determined according to the importance degree of the nodes, and further the information transmission reliability of the operation and management of the power grid is ensured.
In this embodiment, the above determining method is applied to solve the practical problem of network security, and the determining method of the importance of the network node in this embodiment includes the following operations: s11b, S12b, S13b, S14b and S15 b.
In operation S11b, 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 an internet network, and the nodes are internet nodes.
In operation S12b, the number of one-hop object nodes and the number of two-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 other nodes in the target network, so as to obtain a ratio α of the number of all one-hop object nodes and the number of two-hop object nodes occupying the total number of object nodes respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes.
In operation S13b, each node in the target network is used as a random walk initial node according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S14b, the relative importance degree of all nodes of the target network is determined according to the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S15b, a core internet node is determined based on the relative importance of all nodes to enhance the protection level of the core internet node.
The above-described operations S11 b-S14 b may be performed as described above with reference to operations S11-S14. In operation S15b, for example, an internet node in the internet network having a degree of importance score greater than a set score may be determined as a core internet node. The internet nodes with the top 10%, 20% or other set percentage of importance in the internet network can also be determined as core internet nodes. Operation S15b in the present disclosure determines the core internet node based on the correlation of the relative importance degree of all the nodes with the core internet node, and the determination method may be other ways and is not limited to the above-listed embodiments.
Fig. 6 is a flowchart illustrating a method for determining importance of a network node in yet another application scenario according to an embodiment of the present disclosure.
In another scene, the selection of the intersection nodes of the urban complex traffic network has important significance on the structural stability and the control effectiveness of the traffic network. In the urban traffic network, the random attack may be a traffic accident, an intersection traffic light failure, traffic control and the like, and when a key node of the urban traffic complex network is selectively attacked, 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-mentioned determining method is applied to solve the practical problem of traffic grooming control, and the determining method of the importance of the network node in this embodiment includes the following operations: s11c, S12c, S13c, S14c and S15 c.
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 vehicles running on the edges correspond to the weights of the edges.
In operation S12c, the number of one-hop object nodes and the number of two-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 other nodes in the target network, so as to obtain a ratio α of the number of all one-hop object nodes and the number of two-hop object nodes occupying the total number of object nodes respectively1、α2The total number of the object nodes is equal to the number of the object nodes of one hop and the number of the object nodes of two hops of all the nodesThe sum of the number of object nodes.
In operation S13c, each node in the target network is used as a random walk initial node according to a preset restart probability, a random walk initial node vector, and a proportional value α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
In operation S14c, the relative importance degree of all nodes of the target network is determined according to the steady-state probability of the random walk initial node walking to each node of the target network.
At operation S15c, a key traffic node is determined based on the relative importance of all nodes to enhance control and evacuation of the key traffic node.
The above-described operations S11 c-S14 c may be performed as described above with reference to operations S11-S14. In operation S15c, for example, an intersection or cell node in the traffic network having an importance degree score greater than a set score may be determined as a key traffic node. Intersection or cell nodes with 10%, 20% or other set percentage of top importance in the traffic network can also be determined as key traffic nodes. Operation S15c in the present disclosure determines the key traffic node based on the correlation of the relative importance degree of all the nodes with the key traffic node, and the determination method may be other ways and is not limited to the above-listed embodiments.
Based on the same technical concept, a second exemplary embodiment of the present disclosure provides a determination apparatus of network node importance.
Fig. 7 is a block diagram illustrating a structure of a device for determining importance of a network node according to an embodiment of the present disclosure.
Referring to fig. 7, the above-described determination device 2 includes: a probability transition matrix module 21, a one-hop 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 connection number between nodes in the target network.
One hop andthe two-hop coefficient determining module 22 is configured to determine the number of the one-hop object nodes and the number of the 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 other nodes in the target network, so as to obtain a proportional value α where all the number of the one-hop object nodes and the number of the two-hop object nodes respectively occupy the total number of the object nodes1、α2. The total number of the object nodes is equal to the sum of the number of the object nodes of one hop and the number of the object nodes of two hops of all the nodes.
The steady-state probability determination module 23 is configured to use each node in the target network as a random walk initial node, and determine a ratio α according to a preset restart probability, a random walk initial node vector, and a preset restart probability1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
The relative importance level determining module 24 is configured to determine the relative importance levels of all nodes of the target network according to the steady-state probability of the random walk initial node walking to each node of the target network.
Based on the same technical concept, a third exemplary embodiment of the present disclosure provides an abnormality detection apparatus of a biological tissue.
Fig. 8 is a block diagram illustrating a structure of an abnormality detection apparatus for 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-hop 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 the connection relationship and the number of connections 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 node of the target network is a neuron, wherein the standard biological tissue is a normal tissue, and the network structure of the neuron of the biological tissue to be detected is the same as that of the neuron 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 other nodes in the target network, so as to obtain a proportional value α where the number of all the first-hop object nodes and the number of the second-hop object nodes respectively occupy the total number of the object nodes1、α2. The total number of the object nodes is equal to the sum of the number of the object nodes of one hop and the number of the object nodes of two hops of all the nodes.
The steady-state probability determination module 33 is configured to use each node in the target network as a random walk initial node, and determine a ratio α according to a preset restart probability, a random walk initial node vector, and a preset ratio α1Proportional value alpha2And the probability transition matrix determines the steady-state probability of the random walk initial node walking to each node of the target network.
The relative importance level determining module 34 is configured to determine the relative importance levels of all nodes of the target network according to the steady-state probability of the random walk initial node walking to each node of the target network.
The abnormality determining module 35 is configured to determine whether there is an abnormality in the biological tissue to be detected according to whether the relative importance degrees of all the 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.
For example, the biological tissue to be detected is brain tissue of a subject to be detected, and the standard biological tissue is a normal brain tissue sample, where the brain tissue sample and the network structure of the neurons of the brain tissue of the subject to be detected are the same, that is, the structure of the target network is the same, if an abnormality occurs in the internal structure or the internal functional unit of one of the neurons, 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 determining module 35, if the relative importance degree of all the nodes of the neural network of the standard biological tissue is not consistent with the relative importance degree of the corresponding node 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 has an abnormality.
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 part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of 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 a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the probability transition matrix module 21, the one-hop and two-hop coefficient determination module 22, the steady-state probability determination module 23, and the relative importance level determination module 24 may be combined in one module to be implemented, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the probability transition matrix module 21, the one-hop 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 partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-a-package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of three implementations of software, hardware, and firmware, or in any suitable combination of any of them. Alternatively, at least one of the probability transition matrix module 21, the one-hop 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 a computer program module which, when executed, may perform a corresponding function.
A fourth exemplary embodiment of the present disclosure provides an electronic apparatus. The electronic device includes: one or more processors; and 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 any of the determination methods described above.
The method flows according to embodiments of the present disclosure may be implemented as computer software programs. 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 containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by a processor, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
A fifth exemplary embodiment of the present disclosure provides a computer-readable storage medium. The above-described 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 included in the electronic device described in the above embodiments; or may exist separately without being assembled 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 present 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 flowchart 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 specification and claims to modify a corresponding element does not by itself connote any ordinal number of the element or any ordering of one element from another or the order of manufacture, and the use of the ordinal numbers is only used to distinguish one element having a certain name from another element having a same name.
Furthermore, the word "comprising" or "comprises" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A method for determining importance of a network node, comprising:
determining a probability transfer 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 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 of the total number of object nodes occupied by the number of the one-hop object nodes and the number of the two-hop object nodes respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes;
taking each node in the target network as a random walk initial node, and according to a preset restart probability, a random walk initial node vector and the proportional value alpha1The proportional value alpha2Determining the steady-state probability of each node of the target network walked by the random walk initial node according to the probability transfer matrix; and
and determining the relative importance degree of all nodes of the target network according to the steady-state probability of each node which is walked to the target network by the random walk initial node.
2. The method according to claim 1, wherein the determining the probability transition matrix according to the connection relationship and the number of connections between nodes in the target network comprises:
obtaining elements in an empowerment 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 and the number relation of the connection;
determining the total number of connections between each node and all the other nodes in the target network based on the elements of the weighted adjacent matrix to obtain a network degree matrix DG(ii) a And
according to the weighted adjacency matrix W and the network degree matrix DGDetermining a probability transition matrix P, the probability transition matrix satisfying the following expression:
3. the method according to claim 1, wherein the determining the number of the one-hop object nodes and the number of the 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 other nodes in the target network comprises:
taking each node in the target network as a current node, and determining the number of specific nodes having one-way connection relation with the current node as the number of one-hop object nodes of the current node in the rest nodes; and determining the number of specific nodes having a two-way connection relation with the current node in the other nodes as the number of two-hop object nodes of the current node.
4. The determination method according to claim 1, wherein the steady-state probability of the random walk initial node walking to each node of the target network satisfies the following expression:
limt→∞rt＝(1-c)(I-c(α1PT+α2(P2)T))-1r0，
wherein r istRepresenting a steady-state probability matrix at the time t, wherein elements in the steady-state probability matrix are the steady-state probability of each node in the target network from the random walk initial node; lim represents the limit at which the time t tends to infinity; (1-c) represents a restart probability; i represents an identity matrix; p represents a probability transition matrix; p2Representing the square of a probability transition matrix; the superscript T represents the transpose of the matrix; the upper superscript-1 outside the bracketing represents the inverse of the matrix; r is0Representing a random walk initial node vector.
5. The method for determining according to claim 1, wherein determining the relative importance of all nodes of the target network according to the steady-state probability of the random walk initial node walking to each node of the target network 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, 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 added to obtain the importance degree score of the ith node; and
and sequencing all the nodes in the target network based on the importance degree scores of the N nodes in the target network to obtain the relative importance degrees of all the nodes in the target network.
6. The determination method according to claim 1,
the target network is a document citation network, and the nodes of the target network are documents; the determination method further comprises: determining a high-value document based on the relative importance degree of all nodes; or,
the target network is an internet network, and the nodes of the target network are internet nodes; the determination method further comprises: determining core internet nodes based on the relative importance of all nodes to enhance the protection level of the core internet nodes; or,
the target network is a traffic network, nodes of the target network are intersections or cells, streets or roads correspond to connecting edges between the nodes, and obstacles encountered by vehicles running on the connecting edges correspond to the weight of the connecting edges; the determination method further comprises: key traffic nodes are determined based on the relative importance of all nodes to enhance control and evacuation of the key traffic nodes.
7. An apparatus for determining importance of a network node, comprising:
the probability transfer matrix module is used for determining a probability transfer matrix according to the connection relation and the connection quantity among all nodes in the target network;
a first-hop and second-hop coefficient determining module, 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 other nodes in the target network, so as to obtain a ratio α 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 respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes;
a steady-state probability determining module, configured to use each node in the target network as a random walk initial node, and determine the ratio α according to a preset restart probability, a random walk initial node vector, and the ratio α1The proportional value alpha2Determining the steady-state probability of each node of the target network walked by the random walk initial node according to the probability transfer matrix; and
and the relative importance degree determining module is used for determining the relative importance degrees of all the nodes of the target network according to the steady-state probability of each node which is walked to the target network by the random walk initial node.
8. An abnormality detection device for biological tissue, characterized by comprising:
a probability transition matrix module, configured to determine a probability transition matrix according to a connection relationship and a connection number between nodes in a target network, where the target network includes: the target network node is a neuron, wherein the standard biological tissue is a normal tissue, and the network structure of the neuron of the biological tissue to be detected is the same as that of the neuron of the standard biological tissue;
a first-hop and second-hop coefficient determining module, 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 other nodes in the target network, so as to obtain a ratio α 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 respectively1、α2The total object node number is equal to the sum of the one-hop object node number and the two-hop object node number of all the nodes;
a steady-state probability determining module, configured to use each node in the target network as a random walk initial node, and determine the ratio α according to a preset restart probability, a random walk initial node vector, and the ratio α1The proportional value alpha2Determining the steady-state probability of each node of the target network walked by the random walk initial node according to the probability transfer matrix;
the relative importance degree determining module is used for determining the relative importance degrees of all nodes of the target network according to the steady-state probability of each node which is walked to the target network by the random walk initial node; and
and the abnormality determining module is used for determining whether the biological tissue to be detected is abnormal or not according to whether the relative importance degrees of all the 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 or not.
9. An electronic device, comprising:
one or more processors; and
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 determination method of any one of claims 1-6.
10. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the determination method of any one of claims 1-6.
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