Community discovery information processing method and system based on fuzzy mathematics

Abstract

The invention belongs to the technical field of computer networks, and discloses a community discovery information processing method and system based on fuzzy mathematics, and the method comprises thesteps: carrying out community division of all nodes in a network through employing an initialized community algorithm based on node density, and dividing the whole network into unit community sets and isolated node sets; and classifying and combining the unit community sets by using a fuzzy clustering analysis method or a local modularity method to form a community set, and combining isolated nodes by using the local modularity method and a node influence method until all nodes are classified into a community. Community division is made on four different networks through experiments, a division result is visually described by using a network community graph, and the division result is comprehensively compared with evaluation indexes discovered by other algorithms for communities, so thatthe advantages and disadvantages of the algorithm are obtained.

Description

technical field [0001] The invention belongs to the technical field of computer networks, and relates to a community discovery information processing method and system based on fuzzy mathematics. Background technique [0002] Currently, the closest prior art: [0003] A network is called a graph in the field of mathematics or computer. A complex network refers to a network with a large number of network nodes and complex relationships between nodes. A paper published in June 1998 pointed to the small-world property of complex networks (Watts et al., 1998). In October 1999, another paper published showed the scale-free nature of complex networks (Albert et al., 1999). The publication of the above two famous papers ushered in a new era in complex network research. Common networks in daily life, including social networks, computer networks, animal social networks, and social networks, can all be regarded as applications of complex networks in real life. [0004] The theory...

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Problems solved by technology

[0016] (2) In view of the fact that there are still many problems to be solved in the community, there are several aspects that need to be improved. The first is the evaluation index of community division. The current indicators are still not enough to perfectly evaluate whether the community division is correct or not. The second is the community Adaptive methods for community discovery. At present, most algorithms for community discovery are based on knowing certain prior information for community division. In the practical application of existing technologies for community discovery, there are currently many algorithms for community discovery, but most Most of them are only at the theoretical level, and there is no relevant application and practice based on community discovery

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