Method for measuring influence of social network

Abstract

The invention discloses a method for measuring the influence of a social network, wherein the method is used for effectively selecting seed nodes. An algorithm 1 displays a pseudo code of a CCIM algorithm. Firstly, a network G (V, E) is divided into M communities through a community detection algorithm, then the influence of nodes is calculated, and a seed node with the maximum influence is found.For avoiding repeated calculation, incremental calculation of a marginal gain strategy is adopted. And after the seed node is selected, the overlapping influence is deleted and the influence of othernodes Is recalculated. And finally, the seed node propagates the influence in the network by using a specific diffusion model to maximize the influence range.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a method for measuring the influence of a social network. Background technique [0002] In recent years, the rapid development of Internet technology has promoted the development of social networks such as Twitter, Weibo and WeChat. A social network is a network with intricate relationships among individuals that facilitates the dissemination of information among individuals. The purpose of Influence Maximization (IM) is to determine a certain number of the most influential users, and maximize the expected number of ultimately affected users through information diffusion. Due to its wide range of practical applications, such as viral marketing [1,2], rumor control [3,4] and cascade detection [5], influence maximization has drawn great attention from researchers and experts. [0003] The IM problem was first proposed by Kempe et al. [6], who proved that it is an NP-hard pro...

AI Technical Summary

Problems solved by technology

However, the existing community-based influence maximization methods only consider the number of nodes in the community and ignore the density

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