Community-based influence maximization for viral marketing

Derived from the idea of word-to-mouth advertising and with applying information diffusion theory, viral marketing attracts wide research interests because of its business value. As an effective marketing strategy, viral marketing is to select a small set of initial users based on trust among close social circles of friends or families so as to maximize the spread of influence in the social network. In this paper, we propose a new community-based influence maximization method for viral marketing that integrates community detection into influence diffusion modeling, instead of performing community detection independently, to improve the performance. We first build a comprehensive latent variable model which captures community-level topic interest, item-topic relevance and community membership distribution of each user, and we propose a collapsed Gibbs sampling algorithm to train the model. Then we infer community-to-community influence strength using topic-irrelevant influence and community topic interest, and further infer user-to-user influence strength using community-to-community influence strength and community membership distribution of each user. Finally we propose a community-based heuristic algorithm to mine influential nodes that selects the influential nodes with a divide-and-conquer strategy, considering both topic-aware and community-relevant to enhance quality and improve efficiency. Extensive experiments are conducted to evaluate effectiveness and efficiency of our proposals. The results validate our ideas and show the superiority of our method compared with state-of-the-art influence maximization algorithms.