Detecting Influence Relationships from Graphs

Abstract

Graphs have been widely used to represent objects and object connections in applications such as the Web, social networks, and citation networks. Mining influence relationships from graphs has gained interests in recent years because providing influence information about the object connections in graphs can facilitate graph exploration, graph search, and connection recommendations. In this paper, we study the problem of detecting influence aspects, on which objects are connected, and influence degree (or influence strength), with which one graph node influences another graph node on a given aspect. Existing techniques focus on inferring either the influence degrees or influence types from graphs. We propose two generative Aspect Influence Models, OAIM and LAIM, to detect both influence aspects and influence degrees. These models utilize the topological structure of the graphs, the text content associated with objects, and the context in which the objects are connected. We compare these two models with one baseline approach which considers only the text content associated with objects. The empirical studies on citation graphs and networks of users from Twitter show that our models can discover more effective results than the baseline approach.