社交网络影响最大化问题研究综述

Review of Influence Maximization on Social Network

影响最大化问题是社会影响分析中的一个重点研究内容。具体问题是如何从社交网络图中寻找k个初始节点开始传播信息,使得信息的最终传播范围最广。由于影响最大化可以应用于许多的现实应用中,在过去几年里,该问题引起研究者的广泛关注。概述影响最大化问题使用的几种公认的传播模型,其描述信息的传播过程;接着介绍影响最大化问题的几种常用解决算法:基于贪心算法的算法、基于启发式的算法、基于反向影响采样的算法;最后简要概述该问题所面临的难点和未来的研究方向。

Influence maximization is a key research content in social influence analysis.The specific problem is how to find the k initial nodes in the social network graph to spreading the information,so that the final spread of the information is the widest.Because influence maximization can be applied to many real-world applications,this issue has attracted widespread attention from researchers in the past few years.This article outlines several recognized propagation models used in influence maximization problems,which describes the process of information dissemination;then introduces several commonly used algorithms for influence maximization problems:greedy algorithm-based algorithms,heuristic-based algorithms,reverse influence sampling;finally,a brief overview of the difficulties faced by this problem and future re⁃search directions.