Social influence and its applications : an algorithmic and data mining study

Social influence occurs when one’s actions are affected by others. While the study of social influence has a long history in the fields of sociology and marketing, the first study on it from an algorithmic and data mining perspective was performed in 2001. Since then, it has attracted tremendous attention, both in research and in industry. If leveraged carefully, social influence can be exploited in many applications like viral marketing (or targeted advertising in general), recommender systems, analysis of information diffusion in Social Media like Twitter and Facebook, events detection, experts finding, link prediction, ranking of feeds etc. One of the fundamental problems in this fascinating field is the problem of influence maximization, primarily motivated by the application of viral marketing. The objective is to identify a small set of users (called seed set) in a social network, who when convinced to adopt a product will influence others in the network leading to a large number of adoptions in an expected sense. The vision of our work is to take the algorithmic and data mining aspects of viral marketing out of the lab. We organize ours goals and contributions into four categories: (i) With the ever-increasing scale of online social networks, it is extremely important to develop efficient algorithms for influence maximization. We propose two algorithms – Celf++ and Simpath that significantly improve the scalability. In particular, Simpath outperforms the previous state of the art on all three fronts – running time, memory consumption and the quality of the seed set chosen. (ii) We remark that previous studies often make unrealistic assumptions and rely on simulations, instead of validating models against real world data. For instance, they assume an arbitrary assignment of influence probabilities in their studies which focused more on algorithms than on quality and validity with respect to real data. We attack the problem of learning influence probabilities. One of our key findings is that the probabilities are not static and change over time. In view of this, in another work, we propose a novel data driven approach to influence models. We compare our influence model with previous ones and show that it predicts influence diffusion with much better accuracy. (iii) Next, we propose alternative problem formulations – Mintss and Mintime and show interesting theoretical results. These problem formulations capture the problem of deploying viral campaigns on budget and time constraints. In an additional work, we take a fresh perspective on identifying community leaders who may have high influence within their community, using a pattern mining approach. (iv) Finally, we examine applications of social influence. First, we look at the application of viral marketing. Using real world datasets, we show that product adoption is not exactly influence. Given this, we develop a product adoption model and study the problem of maximizing product adoption. Furthermore, we propose and investigate a novel problem in recommender systems, for targeted advertising – Recmax. In recommender systems, the flow of influence (or information) happens not via explicit friendship (social) links, but based on recommendation system models. Hence, targeted advertising in recommender systems requires these information channels are being taken in account.