Directional local group discovery method

Abstract

The invention provides a directional local group discovery method comprising the following steps that a network adjacent matrix and an attribute matrix are established; an attribute importance weight vector is inferred based on the model node of a response directional target; a network edge weight is weighted again based on the weight vector; edges with significantly large weights are extracted on the network weighted again so as to form group seeds; directional local groups are extracted through local extension of group seed optimization weighted conductance; and unimportant and repeated groups in the extracted directional local groups are removed. A directional target self-adaptive inference method is provided by aiming at the characteristics that social network group structures are diverse and group application targets are clear, the network is weighted again under the inferred targeted subspace and the seeds are constructed, the directional local groups are extracted based on local extension, and thus the method is suitable for specific social application targets.

Description

[0001] technology neighborhood [0002] The invention relates to the technical field of social networks, in particular to a method for discovering directional local groups in social networks, which can be used for social network function analysis, structure visualization and various social application input. Background technique [0003] Group discovery in social networks plays an important role in understanding network functions, visualizing network structures, and developing other social applications. From a structural point of view, the connections within groups are tight, while the connections between groups are sparse; from the point of view of attributes, groups are more homogeneous in specific attribute subspaces. [0004] According to the literature search of the prior art, it is found that most of the group discovery methods only consider the network topology information, and only extract a fixed group structure. In fact, due to the complexity and hugeness of social...

AI Technical Summary

Problems solved by technology

However, existing subspaces are usually selected based on unsupervised feature selection mechanisms, which fail to select subspaces for specific targets.

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