Particle swarm random walk-based role identification method

Abstract

The invention relates to a particle swarm random walk-based role identification method. The method comprises the steps of firstly preprocessing collected social network data; secondly performing network structure analysis on the social network data, and performing structured representation on a time sequence network structure by utilizing a time sequence-based dynamic network sub-graph snapshot; thirdly extracting basic features and iterative features from high-dimensional features of slave nodes; fourthly performing particle swarm initialization on the nodes, forming a fitness value matrix based on a feature extraction result of the time sequence network structure, and determining core particles according to the values of fitness functions, wherein each particle is subjected to double guidance by a transfer probability and a fitness objective function, so that different roles are generated; and finally, calculating particle swarm similarity under adjacent time slices, predicting roledistribution of the adjacent time slices, and analyzing a dynamic evolution law of a social network. According to the method, the adaptability and accuracy of large-scale dynamic social network research methods are improved; and the dynamic evolution law of the social network and a role distribution result are effectively predicted.

Description

technical field [0001] The invention relates to a social network analysis technology, in particular to a character recognition method based on particle swarm random walk. Background technique [0002] The development of social behavior networking has accelerated the complexity and dynamics of network structures, such as information interaction networks, scientists' cooperation networks, social networks, and biological networks. The social network analysis method is a sociological analysis method that quantifies the relationship between social network actors. In social network research, role recognition is a very important research problem. It is very important for analyzing and understanding social network structure, understanding the influence of temporal evolution of network structure on roles, predicting user behavior, and studying the relationship between users and the process of information interaction. is of great significance. Behavior individuals play specific role...

AI Technical Summary

Problems solved by technology

According to the Markov logic network, Zhang et al. proposed a network opinion leader role identification method based on relational data, but lacked the consideration of time factors (Identifying network public opinion leaders based on markovlogic networks, 2014)

Character identification based on content analysis usually only focuses on personal information and network information, while ignoring the mining of potential roles. When the theme drifts, it is difficult to achieve the desired effect

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