Community discovery method combining node information and network structure

Abstract

The invention relates to a community discovery method combining node information and a network structure. The method comprises the following specific steps of (1) classifying node characteristics according to the influence of the node characteristics on community division; (2) carrying out content similarity calculation on nodes according to the node characteristics; (3) obtaining an adjacent matrix A of a network according to the network structure; (4) setting a threshold, updating a network weight and generating a weighted network; and (5) setting parameters according to the actual needs, selecting a community discovery algorithm and processing the weighted network obtained in the step (4) to obtain final community division. Through a matrix sum form, the node characteristics and the network structure are fused to a weight form, an unweighted and undirected network is transformed to a weighted and directed network; in addition, through a way of setting the threshold, the unnecessary computation cost is reduced and the time of a community discovery process is saved.

Description

technical field [0001] The invention relates to a community discovery method combining node information and network structure, belonging to the field of network analysis and mining. Background technique [0002] Network analysis has become one of the most important intersecting research fields, and a hotspot in current network research revolves around community structure. The original definition of the concept of community is a group of people with the same characteristics or interests. When starting to use network structure as a model to reflect complex systems in the real world, the concept of community has not only been limited to human relationships, but has gradually expanded to various networks. The task of community discovery is to identify and detect a series of groups composed of nodes in the network. The nodes within these groups are closely connected, while the nodes between groups are relatively sparsely connected, and these groups are called communities. [00...

AI Technical Summary

Problems solved by technology

Although many community detection algorithms have been born for these two situations, these two methods have limitations, and they often only focus on one aspect of node information while ignoring the importance of the other.

The community members obtained by the first method generally have similar feature information but are not closely connected with each other, while the community members obtained by the second method are closely connected but do not have many common features.

This is precisely because these two methods cannot reasonably and completely combine and utilize the effective information of network nodes, so the results of community discovery based on these two methods are not ideal.

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