Influence Maximization in Messenger Based Social Networks
Many online social networks have provided a messenger app (e.g. facebook messenger, direct message on Twitter) to facilitate communication between strong tied friends. Meanwhile, some messenger apps (WeChat) also start to offer social networking services "WeChat Moments" (WM) a.k.a. friend circle) that allow users to post pictures, texts, links of webpages, on their walls, which is called the messenger based social network (Msg SN) In online social networks, Key Opinion Leaders (KOLs) with millions of followers are easy to identify for helping viral marketing/advertising. However, most users of a messenger app have a small number of friends (e.g. hundreds of friends) which makes it challenging to detect a KOL in Msg SN by only counting the number of his/her friends. In this paper, we study the influence maximization problem in the Msg SN of finding the set of most influential KOL nodes that maximize the spread of information. We develop a novel efficient approximation algorithm that calculates the influence by looking at the user's local contribution to the information diffusion process, which scales to large datasets with provable near optimal performance. Experiment results using the real world WeChat Moments data (on January 14th, 2016, 100 thousand users) show that our algorithms can identify the set of KOL nodes with a low time complexity.
2016 IEEE Global Communications Conference (GLOBECOM)