Inferring Social Bridges that Diffuse Information Across Communities
While the accuracy of link prediction has been improved continuously,the utility of the inferred new links is rarely concerned when it comes to information diffusion.This paper defines the utility of links based on average shortest distance and more importantly defines a special type of links named bridge links based on community structure(overlapping or not)of the network.In sociology,bridge links are considered to play a more crucial role in information diffusion across communities.Considering that the accuracy of previous link prediction methods are high in predicting strong ties but not much high in predicting weak ties,we propose a new link prediction method named iBridge,which aims to infer new diffusion paths using biased structural metrics in a supervised learning framework.The experimental results in 3 real online social networks show that iBridge outperforms the traditional supervised link prediction method especially in inferring the bridge links and meantime,the overall performance of predicting bridge links and non-bridge links is not compromised,thus verifying its robustness in inferring new links.
Bridge link prediction Information diffusion Weak ties
Pei Zhang Ke-Jia Chen Tong Wu
Jiangsu Key Laboratory of Big Data Security and Intelligent Processing,Nanjing University of Posts and Telecommunications,Nanjing 210023,Jiangsu,China
国际会议
澳门
英文
475-487
2019-04-14（万方平台首次上网日期，不代表论文的发表时间）