Graph clustering method based on structure entropy constraints

Abstract

Aiming at the problem of how to decode the true structure of the network from the network embedded in the large-scale noise structure at the open information sharing platform centered on big data, and furthermore accurate mining results can be obtained in the mining related information process, the method of clustering based on structure entropy was proposed to realize divide the correlation degree of nodes in the graph. A solution algorithm for calculating two-dimensional structural information and a module division algorithm based on the principle of entropy reduction were proposed to divide the nodes in the graph structure to obtain corresponding modules. The K-dimensional structural information algorithm was used to further divide the divided modules to realize the clustering of nodes in the graph structure. An example analysis shows that the proposed graph clustering method can not only reflect the true structure of the graph structure, but also effectively mine the degree of association between nodes in the graph structure. At the same time, the other three clustering schemes are compared, and the experiment shows that this scheme has higher efficiency in execution time and guarantees the reliability of the clustering results.

Keywords

• data mining
• structural information
• structure entropy
• graph clustering