关键词: 社交网络, 话题感知, 影响力最大化, 线性阈值模型

Abstract: With the continuous emergence of various social networking sites, finding a group of the most influential users on multiple social networks is very important for product recommendation or product promotion. In order to improve the breadth and accuracy of product recommendation or promotion, this paper presents an algorithm of topic-aware influence maximization, M-TLTGreedy. Firstly, this paper evaluates the relation among users based on their text semantics and social relationships in multiple social networks to build a topic-based cross-network graph. Then, based on the linear threshold model, this paper designs a topic-aware influence maximization model across social networks, M-TLT (multiple-topic linear threshold) model. Next, this paper uses the improved heuristic algorithm to select a set of users based on the M-TLT model. Finally, the extensive experiments on real datasets show that the   M-TLTGreedy algorithm performs well on influence spread and running time.

Key words: social networks, topic-aware, influence maximization, linear threshold model