A Study about Social Influence Analysis in Large-scale Networks
Abstract:~ In large social networks, nodes (users, entities) are influenced by others for various reasons. For example, the colleagues have strong influence on one’s work, while the friends have strong influence on one’s daily life. How to differentiate the social influences from different angles(topics)? How to quantify the strength of those social influences? How to estimate the model on real large networks? To address these fundamental questions, we propose Topical Affinity Propagation (TAP) to model the topic-level social influence on large networks. In particular, TAP can take results of any topic modeling and the existing network structure to perform topic-level influence propagation. With the help of the influence analysis, we present several important applications on real data sets such as 1) what are the representative nodes on a given topic? 2) how to identify the social influences of neighboring nodes on a particular node?
Introduction:~ Social network analysis often focus on macro-level models such as degree distributions, diameter, clustering coefficient, communities, small world effect, preferential attachment, etc; work in this area includes. Recently, social influence study has started to attract more attention due to many important applications. However, most of the works on this area present qualitative findings about social influences[14, 16]. In this paper, we focus on measuring the strength of topic-level social influence quantitatively. With the proposed social influence analysis, many important questions can be answered such as 1) what are the representative nodes on a given topic? 2) how to identify topic-level experts and their social influence to a particular node? 3) how to quickly connect to a particular node through strong social ties?. Keep reading on Social Influence Analysis
