基于多特征信息传播模型的微博意见领袖挖掘

在线社交网络中的意见领袖通常是指在社交网络的信息传播中具有较大社会影响力的个体。针对当前意见领袖挖掘方法中只考虑社交网络的拓扑结构和节点的个体属性,缺乏信息传播中交互特征的问题,该文提出了基于扩展独立级联模型,并融入网络结构特征、个体属性和行为特征的意见领袖挖掘模型(extended independent cascade, EIC)。该模型以个体属性、个体在信息传播过程中的交互行为建立加权的传播网络,利用改进的CELF(cost effective lazy forward)算法,挖掘网络中影响力较大的个体。通过实验验证,在意见领袖的扩展核心率指标上,该算法优于拓扑结构类算法,且具有较好的稳定性,同时并未降低意见领袖的传播范围。

Microblog Opinion Leader Mining Based on a Multi-feature Information Diffusion Model

Opinion leaders in online social network are those who have great social influence. Current opinion leader mining methods consider only the topological structure of a social network and the node’s attributes, neglecting the interaction in information diffusion.This paper proposes an opinion leader mining model based on the independent cascade model named EIC (Extended Independent Cascade), which incorporates the network structure, the node attributes, and the user behavior characteristics to build a weighted diffusion network. Experimental results of real data collected from Sina Weibo show that the proposed algorithm is superior to the topological structure algorithms in the extended core rate of opinion leaders, without under-estimate the the scope of influence.