Targeted Influence Maximization Based on Cloud Computing Over Big Data in Social Networks

This paper focuses on the targeted influence maximization based on cloud computing in social networks. Most of existing influence maximization works assume that the influence diffusion probabilities on edges are fixed, and identify the Top-k users to maximize the spread of influence assuming the knowledge of the entire network graph. However, in real-world scenarios, edge probabilities are typically different based on various topics, and may be affected by information received. Meanwhile, obtaining complete network data is difficult due to privacy and computational considerations. Moreover, existing influence maximization algorithms considering target users do not discuss cloud computing which lead to low computational efficiency when dealing with big datasets in social networks. To this end, this paper proposes a targeted influence maximization solution based on cloud computing. First, a new topic-aware model called tag-aware IC model is presented, which takes into account users' interests, characteristics of the item being propagated, and the similarity between users and the related information. Then, efficient algorithms with approximation guarantee are provided using a bounded number of queries to the graph structure. These methods aim to find a seed set that maximizes the expected influence spread over target users who are relevant to given topics. Finally, empirical studies of the proposed algorithms are designed and performed on real datasets. The experimental results show that the techniques in this paper achieves speedup and savings in storage compared with the state-of-the-art methods.