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Dynamic social network community evolution analysis method and system

A technology of social networking and analysis methods, applied in instruments, data processing applications, computing, etc., can solve the problems of distinguishing core node types, failing to make full use of network topology information, etc., to achieve high accuracy and efficiency, and reasonable division results , the effect of high execution efficiency

Active Publication Date: 2019-03-19
CHINA UNIV OF MINING & TECH
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0005] In view of the above analysis, the embodiment of the present invention aims to provide a dynamic social network community evolution analysis method and its system to solve the problems that the prior art fails to make full use of network topology information and does not distinguish the types of core nodes The problem

Method used

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  • Dynamic social network community evolution analysis method and system
  • Dynamic social network community evolution analysis method and system
  • Dynamic social network community evolution analysis method and system

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Embodiment 1

[0086] A specific embodiment of the present invention discloses a method for analyzing the evolution of a dynamic social network community, such as figure 1 shown, including the following steps:

[0087] S1. For a given dynamic social network, starting from the first time slice, divide the community structure corresponding to the time slice network for each time slice;

[0088] S2. Calculate the superspreader set of each time slice network and the superblocker set of each community corresponding to the time slice network according to the community structure division results;

[0089] S3. For the above-mentioned superspreader set, determine the evolution event 1 type of each community; the possible types of the evolution event 1 include generation, merger and expansion events;

[0090] S4. For the above superblocker set, determine the evolution event 2 type of each community; the possible types of the evolution event 2 include disappearance, splitting and shrinking events.

...

Embodiment 2

[0093] Optimizing on the basis of Example 1, such as figure 2 As shown, step S1 can be further refined into the following steps:

[0094] S11. For a given dynamic social network, starting from the first time slice, obtain the neighbor relationship between each node in each time slice network;

[0095] S12. According to the neighbor relationship above, divide the community structure corresponding to each time slice network through the QCA algorithm.

[0096] The QCA algorithm is a fast adaptive dynamic community discovery algorithm based on modularity. It formulates different community structure update strategies for each network change, including node addition, node deletion, edge addition, and edge deletion. By maximizing modularity To determine the community affiliation of incremental nodes.

[0097] Preferably, as image 3 As shown, step S2 can be further refined into the following steps:

[0098] S21. Obtain the superspreader set of each time slice network through the...

Embodiment 3

[0145] Another specific embodiment of the present invention discloses a dynamic social network community evolution analysis system, which uses the method described in Embodiment 1 to perform evolution analysis. Specifically, such as Image 6 As shown, the dynamic social network community evolution analysis system includes a community division module, a superspreader set calculation module, a superblocker set calculation module, a superspreader-based evolution event analysis module, and a superblocker-based evolution event analysis module. Among them, the output of the community division module is connected to the input of the superspreader set calculation module, the superblocker set calculation module, the superspreader-based evolution event analysis module, and the superblocker-based evolution event analysis module. The output end of the superspreader set calculation module is connected to the input end of the superspreader-based evolution event analysis module, and the outp...

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Abstract

The invention relates to a dynamic social network community evolution analysis method and system thereof, belongs to the technical field of network identification, and solves the problems that networktopology structure information cannot be fully utilized and types of core nodes are not distinguished in the prior art. The method comprises the following steps of: for a given dynamic social network, dividing a community structure corresponding to a time slice network for each time slice from a first time slice; calculating a superloader set of each time slice network and a superloader set of each community corresponding to the time slice network according to a community structure division result; determining an evolution event 1 type of each community for the superloader set, i.e., generating, merging and expanding events; and for the superblock set, determining the evolution event type 2 of each community, namely disappearing, splitting and reducing events. According to the method, thecharacteristics of high dissemination of the superloader and the disruption connectivity of the superloader are utilized, the dynamic social network community evolution event is analyzed according tothe change conditions of the two types of node sets, and the evolution event recognition accuracy and efficiency are high.

Description

technical field [0001] The invention relates to the technical field of network identification, in particular to a dynamic social network community evolution analysis method and system thereof. Background technique [0002] Generally, community structures are hidden in complex social networks, users in the same community are closely connected, while users in different communities are relatively sparsely connected. Realistic social networks will change frequently and continuously over time, so only studying the static characteristics of social networks is not enough to characterize its authenticity. At present, researchers are paying more and more attention to the evolution of dynamic social networks, which are used in many fields have a wide range of applications. For example, for a disease transmission network, tracking the community changes of infected patients helps to find the transmission path of the disease, so as to find effective measures to control the spread of the...

Claims

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Application Information

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IPC IPC(8): G06Q50/00
CPCG06Q50/01
Inventor 王志晓徐志鸥席景科袁冠何婧
Owner CHINA UNIV OF MINING & TECH
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