Community discovery method in social network based on maximum group enumeration
A technology for social network and community discovery, applied in the field of big data mining, it can solve the problem that the enumeration speed of large groups is not fast enough, and achieve the effect of performance improvement
Inactive Publication Date: 2019-07-05
HUAZHONG UNIV OF SCI & TECH
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[0005] In view of the defects of the prior art, the purpose of the present invention is to solve the technical problem that the enumeration speed of the maximal clique is not fast enough caused by the dense subgraph divided by the core decomposition method of the prior art
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[0049] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0050] The graph data files abstracted from social networks all contain more than one million nodes, and both scale and complexity have reached a certain level. After research, it is found that despite the large scale of the data set, each social network graph still satisfies a certain pattern, for example, the degree of most users is not high, and the overall structure is sparse; the degree distribution of all users conforms to the exponential distribution, and the degree of a few users is huge; the entire The distance between any two points directly on the graph is not far away, the diameter of t...
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The invention discloses a community discovery method in a social network based on maximum group enumeration, which comprises the following steps: S1, abstracting a social network relationship as a social network graph, and initializing a maximum group result set as a null set; S2, dividing the social network diagram by adopting core division to obtain adjacent sub-diagrams of all vertexes; S3, taking out the next unprocessed adjacent subgraph, judging whether the adjacent subgraph is dense or not, if yes, entering the step S4, and if not, entering the step S5; S4, iteratively deleting the vertex with the minimum degree in the adjacent subgraphs until the current subgraph becomes a cluster, recalling to search for a maximum cluster containing previous deletion points, and merging the obtained maximum cluster into a maximum cluster result set; S5, calling a traditional maximum group enumeration method for calculation, and merging the obtained maximum groups into a maximum group result set; and S6, repeating the steps S3-S5 until all the adjacent subgraphs are processed. According to the characteristic that the adjacency subgraphs are far denser than the whole network, unqualified users are deleted to generate a great group, and the method is efficiently realized on a large-scale social network.
Description
technical field [0001] The invention belongs to the technical field of big data mining, and more specifically, relates to a community discovery method in a social network based on Maximal Clique Enumeration. Background technique [0002] With the development of Internet technology, various virtual communities have gradually grown, such as Facebook, Weibo, and various blog platforms. Virtual communities have become important interpersonal communication tools for many people. A virtual community can be abstracted into a social network graph, in which users are the vertices in the graph, and the relationships between users are the edges between the vertices. Taking microblog as an example, each microblog user can be abstracted into a vertex in the graph, and the "following" between microblog users can be represented by edges in the graph, so that the entire microblog user group can use a social Network diagram to depict. In social networks, community discovery is a classic ap...
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CPCG06Q50/01
Inventor 邵志远金海廖小飞李屹诺
Owner HUAZHONG UNIV OF SCI & TECH