Multi-target community discovering method integrating structure clustering and attributive classification

Abstract

The invention discloses a multi-target community discovering method integrating structure clustering and attributive classification. The method comprises the steps as follows: establishing a network adjacent matrix and an attribute matrix; establishing objective function modularity for measuring structure quality of community division; establishing objective function homogeneity for measuring attribute quality of the community division; initializing a network community division population; using cross and mutation operation to update the community division population; combining a mutated community division population and an external dominance population; finding all dominance community division in a final community division population. The method of the invention designs a function for balancing node attribute classification quality based on Shannon information entropy theory and models an attribute classification problem as an objective function optimization problem. A multi-objective optimization strategy is used to optimize a modularity function for balancing structure clustering quality and a homogeneity function for balancing attribute classification quality to obtain a group of community structures, which are suitable for different applications corresponding to different balances between structure clustering and attribute classification.

Description

technical field

[0001] The invention belongs to the technical field of complex networks, and in particular relates to a multi-objective community discovery method integrating structural clustering and attribute classification in complex networks, which can be used for network function analysis and structure visualization. Background technique

[0002] Community detection methods in complex networks are crucial for understanding the function of the network and visualizing the structure of the network, etc. Generally speaking, a community is a subset of the set of all individuals in the network, and the individuals in the set are similar and dissimilar to the individuals outside the subset.

[0003] According to the literature search of the prior art, most community discovery methods only consider the network topology information, define the community as a collection of closely connected nodes, and use the method of structural clustering to divide the network by using the topo...

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