Dynamic community discovery system fused with sequential network

Abstract

The invention provides a dynamic community discovery system fused with a sequential network, which is characterized in that the data output end of a community data collection module is connected with the data input end of a data processing module, and the data output end of the data processing module is connected with the data input end of a data optimization module; the data output end of the data optimization module is connected with the data input end of the community partition module, and the data output end of the community partition module is connected with the data input end of the data display module; the data optimization module comprises a nonlinear optimization module and a graph optimization module; the data input end of the nonlinear optimization module is connected with the data output end of the data processing module, the data output end of the nonlinear optimization module is connected with the data input end of the graph optimization module, and the data output end of the graph optimization module is connected with the data input end of the community partition module. Compared with a FaceNet method, an SBM + MLE method, a CLBM method and a PisCES method, the PPPM model provided by the invention has the advantages that the accuracy is improved by 5% and 3% on an artificial network and a real network respectively, so that the provided PPPM model has robustness, is reasonable and effective, and can also be applied to the field of common social network community discovery.

Description

technical field [0001] The invention relates to the field of network science and technology, in particular to a dynamic community discovery system integrating time series networks. Background technique [0002] Complex network analysis is gaining increasing attention among researchers in diverse fields such as computer science, social science, biological science, and physical science. Complex networks are always composed of nodes and edges, representing objects and interactions between objects, respectively. For example, in a social network, a node may be a social user, and an edge represents a following or being followed relationship between users. As one of the most important and powerful data structures, analyzing and modeling complex networks can be used in many tasks, such as social interaction pattern analysis, social recommendation and protein functional module identification. So far, the most fundamental tasks in complex networks are node recognition, link predicti...

AI Technical Summary

Problems solved by technology

Currently, no dynamic community discovery algorithm can fully identify and track all these evolutionary events

Images