CN114628041A - Key node identification method and system based on approximate centrality calculation - Google Patents
Key node identification method and system based on approximate centrality calculation Download PDFInfo
- Publication number
- CN114628041A CN114628041A CN202210424369.XA CN202210424369A CN114628041A CN 114628041 A CN114628041 A CN 114628041A CN 202210424369 A CN202210424369 A CN 202210424369A CN 114628041 A CN114628041 A CN 114628041A
- Authority
- CN
- China
- Prior art keywords
- nodes
- centrality
- sequence
- hypergraph
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004364 calculation method Methods 0.000 title claims abstract description 33
- 230000003993 interaction Effects 0.000 claims abstract description 24
- 230000005540 biological transmission Effects 0.000 claims abstract description 14
- 238000004088 simulation Methods 0.000 claims description 10
- 238000007781 pre-processing Methods 0.000 claims description 4
- 201000010099 disease Diseases 0.000 claims description 3
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 3
- 208000015181 infectious disease Diseases 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 230000002596 correlated effect Effects 0.000 claims 2
- 239000011159 matrix material Substances 0.000 description 4
- 230000005541 medical transmission Effects 0.000 description 3
- 230000000644 propagated effect Effects 0.000 description 3
- 208000035473 Communicable disease Diseases 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Medical Informatics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- Epidemiology (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a key node identification method and a system based on approximate centrality calculation, which comprises the steps of firstly constructing an n-path between any two points in a hypergraph network; then selecting nodes in the hypergraph and calculating the proximity centrality; selecting nodes to simulate the transmission process in an SIR transmission model; recording the proportion of the R-state nodes when the propagation reaches a steady state for each propagation source node to obtain a sequence of the proportion of the R-state nodes; and finally, calculating the relevance of the approximate centrality ranking and the proportion of the R-state nodes when the nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality. The invention considers the high-order interaction of different dimensions in the hypergraph, defines the distance between any two nodes and realizes the calculation of the approximate centrality in the hypergraph; the method has important guiding significance for discovering important nodes in the hypergraph network.
Description
Technical Field
The invention particularly relates to a key node identification method and system based on approximate centrality calculation.
Background
Data sets based on the relationship between two objects naturally come from a wide range of fields in the real world: friendships between two users in an online social network, hyperlinks from one web page to another, references from one publication to another, and so forth. Graphical representations allow simple and extensive analysis of this type of relational data sets, and they have been used to understand such data sets in many ways. A thorough understanding of these data sets through graphical analysis may lead to insight behind these data sets and help develop efficient algorithms based on these insights.
Not all relationship networks are limited to pairwise relationships, but multivariate relationships are also ubiquitous; for example, publishing a paper may involve three or more authors, while online group chat may involve tens of participants. Hypergraphs are a natural extension of the traditional graph concept, allowing for edges of various sizes. In form, a hypergraph consists of a set of nodes and a set of hyperedges, where each hyperedge is a non-empty subset containing any number of nodes. They may represent higher-order interactions (i.e., interactions between any number of objects), not just interactions between two objects in a graph. Multiple interactions prove fruitful and indeed unavoidable for challenging tasks in view of the multiple interactions. The harder the task, the more useful is the information to exploit the higher order interactions, which simply reduces to pairwise interactions, which can lead to significant performance degradation. Thus, hypergraphs that naturally represent such complex interactions have attracted considerable attention in many areas, including information dissemination over hypergraphs, computer vision, and graphical learning, among others.
Information diffusion on the network is an important concept in network science, and can be observed in the situations of information dissemination and rumor control in social networks, disease infection among individuals, cascading failures in power grids, and the like. In the diffusion process, some network structures play a key role, mainly affecting the network structure and function. For example, in a social network, people can quickly disseminate information in the network through key nodes; in an infectious disease network, the spread of infectious diseases can be reduced by controlling the impact nodes. Therefore, identifying critical (influential) nodes and edges is of practical significance in network science.
At present, no good solution exists for identifying key nodes on a hypergraph network, and no high-order path-based near-centrality index can identify the key nodes on most data sets.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a key node identification method and system based on approximate centrality calculation. The distance between any two nodes is defined in the hypergraph network, and an efficient algorithm for calculating the node approaching centrality is realized on the basis of the distance definition, so that key nodes in the hypergraph network are found.
The interaction of important nodes in the network plays a key role in information dissemination, and mainly influences the structure and the function of the network. Furthermore, not only the interaction between two nodes may be a pair-wise interaction (i.e., an edge), but also the interaction between three or more nodes may be a high-order interaction. The invention provides a method for calculating the propagation distance between nodes from different high-order interaction dimensions by utilizing the node coincidence degree between the hyperedges in the hypergraph network and the correlation of the high-order interaction, and realizes the calculation of the node approaching the center in the hypergraph on the basis.
A key node identification method based on proximity centrality calculation comprises the following steps:
step 1, constructing an n-path between any two points in a hypergraph network;
step 2, selecting nodes in the hypergraph and calculating the proximity centrality;
step 3, selecting nodes to simulate a transmission process in an SIR transmission model;
and 4, recording the proportion of the R-state nodes when the propagation reaches the steady state for each propagation source node to obtain a sequence of the proportion of the R-state nodes.
And 5, calculating the relevance of the approximate centrality ranking and the proportion of the R-state nodes when the nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality.
Further, the specific method in step 1 is as follows:
defining n-paths of nodes in the hypergraph, defining the n-paths as that any two adjacent hyperedges in the hyperedge sequence have a non-empty intersection with the size of n, and taking the distance between the two nodes of the length of the shortest sequence, wherein the shortest sequence is the n-path between the two nodes.
Further, the specific method in step 2 is as follows:
sequentially selecting nodes in the hypergraph to calculate the high-order interaction proximity centrality containing high k orders, wherein in the hypergraph H ═ V, E, a k order proximity centrality calculation formula of a node i is as follows:
wherein E is a set of the super edges in the hypergraph network, V is a set of nodes in the hypergraph network, and | V | represents the number of the nodes in the set. ds(i, j) represents the distance between node i and node j.
And sequentially calculating k-order recentness of each node, and obtaining a high-order interactive recentness sequence containing k-order after descending order.
Further, the specific method in step 3 is as follows:
and (3) simulating the transmission in the hypergraph network in the step 1 by using a classic disease transmission model SIR, and sequentially selecting nodes from high to low according to the approaching centrality. At an initial time t0Only one node in the network is in an infected state, i.e. the source node is propagated. In the process of propagation, the proportion of the R-state node when the propagation reaches the steady state is recorded for each propagation source node.
Further, the specific method in step 5 is as follows:
calculating the correlation of the approximate centrality correspondence of each candidate propagation source node to the proportion of the R-state nodes by using Kendel correlation
The calculation formula of the Kendel correlation is as follows:
for sequences close to centrality, the ratio of the nodes of the R states, two sets of sequences, aiAnd biThe ith element of the sequence, which is close to the centrality sequence and the R-state node ratio, respectively. (a)i，bi) Denoted as the ith sequence pair. For (a)i，bi) And (a)j，bj) When a isi＞ajAnd b isi＞bjOr ai＜ajAnd b isi＜bjThen, thenAnd recording as consistent, otherwise, recording as inconsistent. n is+Number of identical sequence pairs, n-The number of inconsistent sequence pairs and N is the total number of sequence pairs. Computing
If tau is greater than 0, it shows that it is close to central sequence, and if tau is less than 0, it shows that two groups of sequences are positive correlation.
When the k-order approaching centrality ranking of the nodes in the hypergraph network is higher, the proportion of the R-state nodes is larger when SIR simulation propagation reaches a steady state, the node centrality ranking and the proportion of the R-state nodes when SIR simulation propagation reaches the steady state are in positive correlation, and therefore the nodes with large influence can be identified through the k-order approaching centrality.
The beneficial effects of the invention are:
the invention considers the high-order interaction of different dimensions in the hypergraph, defines the distance between any two nodes and realizes the calculation of the approximate centrality in the hypergraph. The method has important guiding significance for discovering important nodes in the hypergraph network. The invention can provide a new recognition and research method for deeply understanding epidemic propagation process, information propagation promotion in social network, important paper partner discovery and the like, and is helpful for modeling analysis and solving application problems in practical environment.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings and the embodiment.
As shown in fig. 1, a method for identifying a key node based on proximity centrality calculation includes the following steps:
step 1, constructing an n-path (n-path) between any two points in a hypergraph network;
defining n-paths of nodes in the hypergraph, defining the n-paths as that any two adjacent hyperedges in the hyperedge sequence have a non-empty intersection with the size of n, and taking the distance between the two nodes of the length of the shortest sequence, wherein the shortest sequence is the n-path between the two nodes.
Step 2, selecting nodes in the hypergraph and calculating the proximity centrality;
sequentially selecting nodes in the hypergraph to calculate the high-order interaction proximity centrality containing high k orders, wherein in the hypergraph H (V, E), a k order proximity centrality calculation formula of a node i is as follows:
wherein E is a set of the super edges in the hypergraph network, V is a set of nodes in the hypergraph network, and | V | represents the number of the nodes in the set. ds(i, j) represents the distance between node i and node j.
And sequentially calculating k-order recentness of each node, and obtaining a high-order interactive recentness sequence containing k-order after descending order.
Step 3, selecting nodes to simulate a transmission process in an SIR transmission model;
and (3) simulating the transmission in the hypergraph network in the step 1 by using a classic disease transmission model SIR, and sequentially selecting nodes from high to low according to the approaching centrality. At an initial time t0Only one node in the network is in an infected state, i.e. the source node is propagated. In the process of propagation, the proportion of the R-state node when the propagation reaches the steady state is recorded for each propagation source node.
And 4, recording the proportion of the R-state nodes when the propagation reaches the steady state for each propagation source node to obtain a sequence of the proportion of the R-state nodes.
And 5, calculating the relevance of the approximate centrality ranking and the proportion of the R-state nodes when the nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality.
Calculating the correlation of the approximate centrality correspondence of each candidate propagation source node to the proportion of the R-state nodes by using Kendel correlation
The calculation formula of the Kendel correlation is as follows:
for sequences that are close to centrality, sequences with a ratio of R-state nodes two sets of sequences,aiand biThe ith element of the sequence is close to the centrality sequence and the R-state node ratio, respectively. (a)i，bi) Denoted as the ith sequence pair. For (a)i，bi) And (a)j，bj) When a isi＞ajAnd b isi＞bjOr ai＜ajAnd b isi＜bjIf yes, the result is recorded as consistent, otherwise, the result is recorded as inconsistent. n is+Number of identical sequence pairs, n-The number of inconsistent sequence pairs and N is the total number of sequence pairs. Computing
If tau is greater than 0, it shows that it is close to central sequence, and if tau is less than 0, it shows that two groups of sequences are positive correlation.
When the k-order approaching centrality ranking of the nodes in the hypergraph network is higher, the proportion of the R-state nodes is larger when SIR simulation propagation reaches a steady state, the node centrality ranking and the proportion of the R-state nodes when SIR simulation propagation reaches the steady state are in positive correlation, and therefore the nodes with large influence can be identified through the k-order approaching centrality.
A key node identification system based on proximity centrality calculation comprises a preprocessing module, a proximity centrality calculation module, a propagation simulation module and a correlation calculation module.
The preprocessing module is used for constructing a path between any two points in the hypergraph network;
the path in the hypergraph is a hyperedge sequence, wherein two adjacent hyperedges have a non-empty intersection, and the length of the path is the length of the sequence. The distance between two nodes is defined as the length of the shortest super-edge sequence, and the first super-edge and the last super-edge in the shortest super-edge sequence respectively comprise one and the other of the two nodes. The simplest method for defining n-paths of nodes in the hypergraph is to define the n-paths as a plurality of nonrepeating hyperedge sequences with n common nodes between edges where any two nodes are located, and to take the distance between the two nodes of the length of the shortest sequence, wherein the shortest sequence is the path between the two nodes.
In order to calculate the shortest distance between any two nodes in the hypergraph network by using a program, constructing a hyperedge adjacency matrix to represent the hypergraph network in the program, wherein the hyperedge adjacency matrix is an m-m matrix, and an element a in the hyperedge adjacency matrixij(i ≠ j) is 1 or 0, with 1 indicating that hyper-edge i and hyper-edge j contain common nodes and 0 being the opposite.
The near-centrality calculation module is used for selecting nodes in the hypergraph and calculating the near-centrality, and the specific method comprises the following steps:
sequentially selecting nodes in the hypergraph to calculate the high-order interaction proximity centrality containing high k orders, wherein in the hypergraph H ═ V, E, a k order proximity centrality calculation formula of a node i is as follows:
wherein E is a set of hyper-edges in the hyper-graph network, V is a set of nodes in the hyper-graph network, and | V | represents the number of the nodes in the set. ds(i, j) represents the distance between node i and node j.
And sequentially calculating the k-order recentness of each node, and obtaining a k-order high-order interaction recentness sequence after descending order.
The transmission simulation module is used for selecting a node to simulate a transmission process in an SIR transmission model;
and (3) simulating the transmission in the hypergraph network in the step 1 by using a classic disease transmission model SIR, and sequentially selecting nodes from high to low according to the approaching centrality. At an initial time t0Only one node in the network is in an infected state, i.e. the source node is propagated. In the process of propagation, the proportion of the R-state node when the propagation reaches the steady state is recorded for each propagation source node.
The relevance calculating module is used for calculating the relevance of the approximate centrality ranking and the proportion of R-state nodes when the nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality.
Calculating the correlation of the approximate centrality correspondence of each candidate propagation source node to the proportion of the R-state nodes by using Kendel correlation
The calculation formula of the Kendel correlation is as follows:
for sequences close to centrality, the ratio of the nodes of the R states, two sets of sequences, aiAnd biThe ith element of the sequence, which is close to the centrality sequence and the R-state node ratio, respectively. (a)i，bi) Denoted as the ith sequence pair. For (a)i，bi) And (a)j，bj) When a isi＞ajAnd b isi＞bjOr ai＜ajAnd b isi＜bjIf yes, the result is recorded as consistent, otherwise, the result is recorded as inconsistent. n is+Number of identical sequence pairs, n-The number of inconsistent sequence pairs and N is the total number of sequence pairs. Computing
If tau is greater than 0, it shows that it is close to central sequence, and if tau is less than 0, it shows that two groups of sequences are positive correlation.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.
Claims (6)
1. A key node identification method based on proximity centrality calculation is characterized by comprising the following steps:
step 1, constructing an n-path between any two points in a hypergraph network;
step 2, selecting nodes in the hypergraph and calculating the proximity centrality;
step 3, selecting nodes to simulate a transmission process in an SIR transmission model;
step 4, recording the proportion of the R-state nodes when the propagation reaches the steady state for each propagation source node to obtain a sequence of the proportion of the R-state nodes;
and 5, calculating the relevance of the approximate centrality ranking and the proportion of the R-state nodes when the nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality.
2. The method for identifying key nodes based on proximity centrality calculation according to claim 1, wherein the specific method in step 1 is as follows:
defining n-paths of nodes in the hypergraph, defining the n-paths as that any two adjacent hyperedges in the hyperedge sequence have a non-empty intersection with the size of n, and taking the distance between the two nodes of the length of the shortest sequence, wherein the shortest sequence is the n-path between the two nodes.
3. The method for identifying key nodes based on proximity centrality calculation according to claim 2, wherein the method in step 2 is as follows:
sequentially selecting nodes in the hypergraph to calculate the high-order interaction proximity centrality containing high k orders, wherein in the hypergraph H ═ V, E, a k order proximity centrality calculation formula of a node i is as follows:
wherein E is a set of the super edges in the hypergraph network, V is a set of nodes in the hypergraph network, and | V | represents the number of the nodes in the set; ds(i, j) represents the distance of node i and node j;
and sequentially calculating the k-order recentness of each node, and obtaining a k-order high-order interaction recentness sequence after descending order.
4. The method for identifying key nodes based on proximity centrality calculation according to claim 3, wherein the specific method in step 3 is as follows:
simulating the propagation in the hypergraph network in the step 1 by using a classical disease propagation model SIR, and sequentially selecting nodes from high to low according to the approaching centrality; at an initial time t0Only one node in the network is in the quiltThe state of infection, i.e., the source node of propagation; in the process of propagation, the proportion of the R-state node when the propagation reaches the steady state is recorded for each propagation source node.
5. The method for identifying key nodes based on proximity centrality calculation as claimed in claim 4, wherein the specific method in step 5 is as follows:
calculating the correlation of the approximate centrality correspondence of each candidate propagation source node to the proportion of the R-state nodes by using Kendel correlation
The calculation formula of the Kendel correlation is as follows:
for sequences close to centrality, the ratio of the nodes of the R states, two sets of sequences, aiAnd biThe ith element of the sequence, which is close to the centrality sequence and the R-state node ratio, respectively; (a)i，bi) Marking as the ith sequence pair; for (a)i，bi) And (a)j，bj) When a isi>ajAnd b isi>bjOr ai<ajAnd b isi<bjIf yes, marking as consistent, otherwise, marking as inconsistent; n is+Number of identical sequence pairs, n-The number of the inconsistent sequence pairs is N, and the total number of the sequence pairs is N; computing
τ>0 indicates a nearly central sequence, two groups of sequences B are positively correlated, and tau<0 indicates that the sequences of the A group and the B group are in negative correlation;
when the k-order approaching centrality ranking of the nodes in the hypergraph network is higher, the proportion of the R-state nodes is larger when SIR simulation propagation reaches a steady state, the node centrality ranking and the proportion of the R-state nodes when SIR simulation propagation reaches the steady state are in positive correlation, and therefore the nodes with large influence can be identified through the k-order approaching centrality.
6. A key node identification system based on proximity centrality calculation is characterized by comprising a preprocessing module, a proximity centrality calculation module, a propagation simulation module and a correlation calculation module;
the preprocessing module is used for constructing a path between any two points in the hypergraph network, and the specific method comprises the following steps:
the path in the hypergraph is a hyperedge sequence, wherein two adjacent hyperedges have a non-empty intersection, and the length of the path is the length of the sequence; the distance between two nodes is defined as the length of the shortest super-edge sequence, and the first super-edge and the last super-edge in the shortest super-edge sequence respectively comprise one node and the other node; the simplest method for defining n-paths of nodes in the hypergraph is to define the nodes as a plurality of nonrepeating hyperedge sequences with n public nodes between the edges of any two nodes, and to take the distance between the two nodes of the length of the shortest sequence, wherein the shortest sequence is the path between the two nodes;
the near-centrality calculation module is used for selecting nodes in the hypergraph and calculating the near-centrality, and the specific method comprises the following steps:
sequentially selecting nodes in the hypergraph to calculate the high-order interaction proximity centrality containing high k orders, wherein in the hypergraph H ═ V, E, a k order proximity centrality calculation formula of a node i is as follows:
wherein E is a set of the super edges in the hypergraph network, V is a set of nodes in the hypergraph network, and | V | represents the number of the nodes in the set; ds(i, j) represents the distance of node i and node j;
sequentially calculating k-order recentness of each node, and obtaining a high-order interactive recentness sequence containing k orders after descending order arrangement;
the propagation simulation module is used for selecting nodes to simulate the propagation process in an SIR propagation model;
simulating the propagation in the hypergraph network in the step 1 by using a classical disease propagation model SIR, and sequentially selecting nodes from high to low according to the approaching centrality; at an initial time t0With only one node in the networkIn an infected state, i.e., a propagation source node; in the process of propagation, recording the proportion of the R-state nodes when the propagation reaches the steady state aiming at each propagation source node;
the relevance calculating module is used for calculating the relevance of the approximate centrality ranking and the proportion of R-state nodes when the R-state nodes reach the steady state in a spreading mode, and identifying the nodes with large influence through k-order approximate centrality;
calculating the correlation of the approximate centrality correspondence of each candidate propagation source node to the proportion of the R-state nodes by using Kendel correlation
The calculation formula of the Kendel correlation is as follows:
for sequences close to centrality, the ratio of the nodes of the R states, two sets of sequences, aiAnd biThe ith element of the sequence, which is the approximate centrality sequence and the R-state node proportion, respectively; (a)i，bi) Recording as the ith sequence pair; for (a)i，bi) And (a)j，bj) When a isi>ajAnd b isi>bjOr ai<ajAnd b isi<bjIf yes, marking as consistent, otherwise, marking as inconsistent; n is+Number of identical sequence pairs, n-The number of the inconsistent sequence pairs is N, and the total number of the sequence pairs is N; computing
τ>0 indicates a nearly central sequence, and the two sequences are positively correlated, tau<0 indicates that the sequences of the two groups A and B are in negative correlation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210424369.XA CN114628041A (en) | 2022-04-21 | 2022-04-21 | Key node identification method and system based on approximate centrality calculation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210424369.XA CN114628041A (en) | 2022-04-21 | 2022-04-21 | Key node identification method and system based on approximate centrality calculation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114628041A true CN114628041A (en) | 2022-06-14 |
Family
ID=81905834
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210424369.XA Pending CN114628041A (en) | 2022-04-21 | 2022-04-21 | Key node identification method and system based on approximate centrality calculation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114628041A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117811992A (en) * | 2024-02-29 | 2024-04-02 | 山东海量信息技术研究院 | Network bad information propagation inhibition method, device, equipment and storage medium |
-
2022
- 2022-04-21 CN CN202210424369.XA patent/CN114628041A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117811992A (en) * | 2024-02-29 | 2024-04-02 | 山东海量信息技术研究院 | Network bad information propagation inhibition method, device, equipment and storage medium |
| CN117811992B (en) * | 2024-02-29 | 2024-05-28 | 山东海量信息技术研究院 | Network bad information propagation inhibition method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kumar et al. | Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach | |
| Newman | The physics of networks | |
| CN110532436B (en) | Cross-social network user identity recognition method based on community structure | |
| Prakash et al. | Threshold conditions for arbitrary cascade models on arbitrary networks | |
| Alvari et al. | Community detection in dynamic social networks: A game-theoretic approach | |
| Lin et al. | Measuring agility of networked organizational structures via network entropy and mutual information | |
| CN114928548A (en) | Social network information propagation scale prediction method and device | |
| Li et al. | Social network user influence dynamics prediction | |
| CN114628041A (en) | Key node identification method and system based on approximate centrality calculation | |
| CN111612641A (en) | Method for identifying influential user in social network | |
| Dey et al. | A survey on the role of centrality as seed nodes for information propagation in large scale network | |
| Ma et al. | An ensemble of random graphs with identical degree distribution | |
| Hajarathaiah et al. | Efficient algorithm for finding the influential nodes using local relative change of average shortest path | |
| Sun et al. | Finding critical nodes in a complex network from information diffusion and Matthew effect aggregation | |
| Xiong et al. | Information diffusion model in modular microblogging networks | |
| Feng et al. | Recovering information recipients in social media via provenance | |
| Paolillo | Network analysis | |
| CN108595684A (en) | A kind of overlapping community discovery method and system based on preferential learning mechanism | |
| Regan | Networks: Structure and dynamics | |
| Chuan et al. | Design and implementation of information dissemination simulation algorithm in large-scale complex network based on spark | |
| Sen | Complexities of social networks: A Physicist's perspective | |
| Chen et al. | Inferring full diffusion history from partial timestamps | |
| Deng et al. | Novel node centrality-based efficient empirical robustness assessment for directed network | |
| CN110188098A (en) | A kind of high dimension vector data visualization method and system based on the double-deck anchor point figure projection optimization | |
| Raju et al. | CILPA: a cohesion index based label propagation algorithm for unveiling communities in complex social networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |