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Community discovery method and system based on Louvain algorithm

A community discovery and community technology, applied in the field of community discovery system based on the Louvain algorithm, can solve problems such as inaccurate results, loose structure, unsuitable data, etc.

Inactive Publication Date: 2018-09-07
SANMENG TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, although many existing clustering methods have combined the structure of the network and the attribute characteristics of nodes (or called node attributes or node attribute information) (for example, constructing new clustering methods by weighting attributes and structures) network, and perform community division on the new network), but the results of these clusters often have communities that are not structurally close or not related, which leads to inaccurate results of community discovery; moreover, the time complexity of these methods High, not suitable for processing large-scale data

Method used

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  • Community discovery method and system based on Louvain algorithm
  • Community discovery method and system based on Louvain algorithm
  • Community discovery method and system based on Louvain algorithm

Examples

Experimental program
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no. 1 example

[0036] see figure 2 , figure 2 A flow chart of the first embodiment of a community discovery method based on the Louvain algorithm of the present invention is shown, which includes:

[0037] S101, initialize the community, and regard each node as a community;

[0038] S102, sequentially assigning each node to the community where each neighbor node is located to construct a community graph;

[0039] Specifically, the step S102 includes:

[0040] (1) Try to allocate each node to the community where each neighbor node is located in turn; preferably, the allocation is performed in a round-robin manner.

[0041] (2) Calculate the change in modularity before and after allocation; where the change in modularity before and after allocation refers to the difference between the modularity before and after allocation.

[0042] (3) Extract the maximum value of the variation of modularity;

[0043] (4) If the maximum value of the modularity change is greater than 0, assign the nodes...

no. 2 example

[0057] see image 3 , image 3 A flow chart of the second embodiment of a community discovery method based on the Louvain algorithm of the present invention is shown, which includes:

[0058] S201, initialize the community, and regard each node as a community;

[0059] S202, sequentially assigning each node to the community where each neighbor node is located to construct a community graph;

[0060] Specifically, the step S202 includes:

[0061] (1) Try to assign each node to the community where each neighbor node is located in turn;

[0062] (2) Calculate the change in modularity before and after allocation; where the change in modularity before and after allocation refers to the difference between the modularity before and after allocation.

[0063] (3) Extract the maximum value of the variation of modularity;

[0064] (4) If the maximum value of the modularity change is greater than 0, assign the nodes to the community, and repeat this step until all nodes do not chang...

no. 1 example

[0074] see Figure 4 , Figure 4 A first embodiment of the community discovery system 100 based on the Louvain algorithm of the present invention is shown, which includes:

[0075] Initialization module 1 is used to initialize the community, and each node is regarded as a community;

[0076] The first building block 2 is used to sequentially assign each node to the community where each neighbor node is located to construct a community graph;

[0077] The second building block 3 is used to regard the community as a node according to the community graph, and reconstruct the community graph;

[0078] The output module 4 is used to output the result when all the states are stable.

[0079] Such as Figure 5 As shown, the first building block 2 includes:

[0080] The allocation unit 21 is configured to try to allocate each node to the community where each neighbor node is located in turn; preferably, the allocation is performed in a polling manner.

[0081] The calculation un...

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Abstract

The invention discloses a community discovery method based on a Louvain algorithm. The method comprises the following steps that: S1: initializing a community, and taking each node as a community; S2:distributing each node to a community where each neighbour node is positioned in sequence to construct a community graph; S3: according to the community graph, taking the community as a node, and re-constructing the community graph; and S4: repeating S3, and outputting a result until all states are stable. The invention also discloses a community discovery system based on the Louvain algorithm. By use of the method and the device, each node in the network is taken as the community, and analysis is carried out by aiming at the modularity and the edge weight of the community so as to obtain more accurate community discovery.

Description

technical field [0001] The present invention relates to the technical field of data mining, in particular to a community discovery method based on the Louvain algorithm and a community discovery system based on the Louvain algorithm Background technique [0002] With the development of information technology, a large number of user information characteristics are stored in the information system, and there is also a certain correlation between users. User features are multi-dimensional and multi-associated. Community discovery can help people understand the structural characteristics of the network more effectively, so as to provide more effective and personalized services. [0003] Currently, many studies discover communities by analyzing the structure of networks. Among them, Blondel et al. proposed an iterative two-stage modularity maximization fast algorithm (BGL algorithm) for community discovery based on the fact that the community structure of large-scale networks i...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 李果钟金顺李永杰王晓嘎宋敏峰
Owner SANMENG TECH CO LTD
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