中国科学院科技战略咨询研究院机构知识库

This paper focuses on the targeted influence maximization based on cloud computing in social networks. Most of existing influence maximizationworks assume that the inuence 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.