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An Overlapping Method for Community Discovery

A discovery method and social network technology, applied in the field of overlapping community discovery, can solve problems such as low efficiency and unsuitable iterative calculation, and achieve the effect of increasing processing power, avoiding performance bottlenecks, and easy horizontal expansion

Active Publication Date: 2022-03-25
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, the nature of MapReduce determines that it is more suitable for batch processing calculations, not suitable for a large number of iterative calculations in graph algorithms, so the efficiency is low

Method used

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  • An Overlapping Method for Community Discovery
  • An Overlapping Method for Community Discovery

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Embodiment

[0023] An overlapping community discovery method, the steps are as follows:

[0024] S1, figure 1 It is a schematic diagram of the original structure of the present invention, such as figure 1 As shown, calculate all the maximal cliques in the graph G, sort each maximal clique in the graph according to the vertex number, use the smallest vertex ID of each maximal clique as the category of the maximal clique, and distribute statistics on all The number of the maximum clique determines the order of the maximum clique according to the order of the maximum clique number, and sends all the information to the computing node.

[0025] The obtained maximal clique is as follows:

[0026] Numbering vertices in a maximal clique Subgroup 0 1，2,3,4 (2,3,4)，(3,4) 1 3,4,5 ╲ 2 2,3,4,6,7 (3,4,6,7)，(4,6,7)，(6,7)

[0027] Count the categories and numbers of all extremely large cliques, number all extremely large cliques consecutively starting from 0, and di...

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Abstract

The invention discloses a method for discovering overlapping communities, the steps are as follows: S1 for all the maximum cliques in the distributed computing graph G, count the information on the number of the maximum cliques and send the information on the number of the maximum cliques to all computing nodes Middle; S2 sorts and codes all the maximal cliques, assigns a unique code to each maximal clique to determine the maximal clique; S3 calculates the subclusters in the maximal clique, and sends the subclusters to different computing nodes; S4 In each computing node, construct an inverted index; S5 uses the inverted index for each extremely large clique to calculate the extremely large clique that has k-1 shared vertices with each extremely large clique and the same computing node, Use the union search set to save the ID of the extremely large group in the same set; S6 merge the union search sets in different clusters to find the corresponding faction. An overlapping community discovery method using the above structure increases the system's ability to process large graphs, making horizontal expansion easier and lower in cost.

Description

technical field [0001] The invention relates to the technical field of graph data analysis, in particular to an overlapping community discovery method. Background technique [0002] In the real world, graphs are widely used to represent entities and the relationships between them. However, graphs in the real world are usually composed of dense subgraphs, that is, a small number of vertices in the graph have close connections. Mining dense subgraphs from graphs is a very basic problem that has been extensively studied in the fields of graph databases and data mining, and there are several different definitions for communities. [0003] Clique filtering has important applications in finding closely related groups in social networks. In social networks, it is of great significance to find closely connected communities, which can be used for intelligent recommendation, advertisement push and risk control in the financial system. Clique is a clique-based structure, but it requ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9536G06F16/901
CPCG06F16/9536G06F16/901
Inventor 任泽槟李荣华王国仁秦宏超金福生
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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