| In modern life,people have a lot of time in the use of the Internet,people in the Internet,as in real life,contact each other,so the formation of a large population base of the network social relations.In such a network,it is important to define the difference between each group accurately.There are real cultures,standards and directions in the Internet community.In this paper,an algorithm is proposed to classify the communities closer to the real situation and make the divided communities more realistic.This paper summarizes and analyzes the methods of previous studies.While referring to the previous methods,a method of community partition is proposed by merging all kinds of information on the Internet.In view of the topology,interest and influence of social network,this method proposes a community discovery algorithm ICDA based on topology structure,interest and user influence.The ICDA algorithm designs a new community discovery algorithm based on topology structure,interest and user influence degree.It uses the topology structure of social network to synthesize the basic information of users and the content published by users in the community.The community can be effectively divided to make the community closer to the actual situation of the network.This paper compares the proposed ICDA algorithm with the classic community division algorithm Louvain algorithm,and compares the three important parameters of interest cohesion,aggregation coefficient and module degree respectively,and validates the feasibility of the ICDA algorithm.At the same time,a simple implementation of the proposed community partition algorithm is carried out,and the effectiveness of the algorithm is preliminarily verified.The main functions are as follows: using Java language to realize the basic functions of data acquisition,and processing the collected data,realizing the community partition algorithm based on ICDA algorithm.Data-Driven Documentstechnology is used to visualize data after partitioning. |
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