Method for realizing community discovery in social networking

Abstract

The invention provides a method for realizing community discovery in social networking. The method comprises the following steps of: for any node in the social networking, computing the dependence of other nodes in the social networking on the node, and finding k nodes with the highest dependence as k neighbors of the node; selecting an unprocessed node from the social networking, and executing the next step until the nodes in the social networking are all processed; and judging whether the k most dependent nodes of the unprocessed node meet the requirements of a k community or not, re-executing the previous step after a community is output if the k most dependent nodes of the unprocessed node meet the requirements of the k community, and if the k most dependent nodes of the unprocessed node do not meet the requirements of the k community, selecting a subset from a set formed by the k most dependent nodes of the unprocessed node, judging whether the selected subset meets the requirements of the k community or not until the community meeting the requirements of the k community is found or all subsets are judged, and re-executing the previous step.

Description

technical field [0001] The invention relates to the field of data mining, in particular to a method for realizing community discovery in social networks. Background technique [0002] Social Networking (SN for short) is a relational network used to represent the interconnection among individuals in society. In recent years, social networks have gained widespread attention in research, and degree distribution analysis, individual ranking, community discovery, and pattern discovery are all typical applications of social networks. In many past studies, many scholars focused on mining and analyzing the importance and influence of individuals in social networks. For example, search engines analyze the link structure of the Web and calculate the importance of web pages, expecting to provide users with search results that best meet their needs; in academic cooperation networks, people expect to discover cooperation patterns through hierarchical analysis of cooperation structures a...

AI Technical Summary

Problems solved by technology

However, the importance of nodes calculated by the random walk model and PageRank model is not comprehensive, for example, it cannot reflect the "indispensability" of nodes in social networks

Some nodes may be easily accessible in the social network, but if these nodes are deleted, the impact on other nodes in the social network may be limited, and the indispensability of such nodes is low

On the contrary, other nodes have a low probability of being visited in the social network, but once deleted, the impact on other nodes in the social network is serious, and the indispensability of such nodes is high

In the prior art, there is a lack of related methods to measure and calculate the "indispensability"

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