Relationship classification in large scale online social networks and its impact on information propagation

In this paper, we study two tightly coupled topics in online social networks (OSN): relationship classification and information propagation. The links in a social network often reflect social relationships among users. In this work, we first investigate identifying the relationships among social network users based on certain social network property and limited pre-known information. Social networks have been widely used for online marketing. A critical step is the propagation maximization by choosing a small set of seeds for marketing. Based on the social relationships learned in the first step, we show how to exploit these relationships to maximize the marketing efficacy. We evaluate our approach on large scale real-world data from Renren network, showing that the performances of our relationship classification and propagation maximization algorithm are pretty good in practice.