Symbol network community discovery method based on structural balance constraint

Abstract

The invention relates to the technical field of social network services, and discloses a symbol network community discovery method based on structural balance constraint. The method comprises the steps of representing a symbol network in a graph form, constructing a symbol network adjacent matrix, setting structural balance constraint information, constructing a community discovery model based onsemi-non-negative matrix decomposition, solving the community discovery model, and obtaining a community discovery result. According to the method, the structure balance constraint relation of nodes is obtained based on the structure balance theory; and then the structure balance constraint relation serving as a regular constraint term is incorporated into a symbol network community discovery model based on semi-non-negative matrix decomposition to solve the node community indication matrix, and finally, a community division result is obtained by judging the node community attribution intensity, so that the community discovery quality is further improved.

Description

technical field [0001] The invention relates to the technical field of social network services, in particular to a symbolic network community discovery method based on structural balance constraints. Background technique [0002] A symbolic network is a complex network in which there are two types of positive and negative node relationships. For example, social networks have user relationships with opposite natures such as trust-distrust, approval-disagreement, and like-dislike. The way to discover the symbolic network community is to partition the network nodes. The nodes in the same partition are required to have positive relationships as much as possible and the links are tight; the nodes between different partitions are required to be as negative as possible and the links are sparse. Symbolic network community discovery can not only help understand the structural characteristics and evolution trends of symbolic networks, but also has important application value, such as ...

AI Technical Summary

Problems solved by technology

Although the existing methods can solve the problem of community discovery in symbolic networks to a certain extent, they all ignore the influence of structural balance theory, an important basic theory in symbolic networks, on improving the performance of community discovery. Therefore, the existing methods of community discovery in symbolic networks there is still room for improvement

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