Mining Initial Nodes with BSIS Model and BS-G Algorithm on Social Networks for Influence Maximization

• 作者: Dejuan Cao Fang Long Hailan Shen Xiaoheng Deng Yan Pan
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: Social networks INFLUENCE MAXIMIZATION Behavior TENDENCY SENTIMENT TENDENCY GREEDY ALGORITHM
• 年，卷（期）: 2017,（2）
• 页码范围: 33-35
• 页数: 3页
• 分类号: C5

摘要

Influence maximization is the problem to identify and find a set of the most influential nodes, whose aggregated influence in the network is maximized. This research is of great application value for advertising,viral marketing and public opinion monitoring. However, we always ignore the tendency of nodes' behaviors and sentiment in the researches of influence maximization. On general, users' sentiment determines users behaviors, and users' behaviors reflect the influence between users in social network. In this paper, we design a training model of sentimental words to expand the existing sentimental dictionary with the marked-commentdata set, and propose an influence spread model considering both the tendency of users' behaviors and sentiment named as BSIS (Behavior and Sentiment Influence Spread) to depict and compute the influence between nodes. We also propose an algorithm for influence maximization named as BS-G (BSIS with Greedy Algorithm) to select the initial node. In the experiments, we use two real social network data sets on the Hadoop and Spark distributed cluster platform for experiments, and the experiment results show that BSIS model and BS-G algorithm on big data platform have better influence spread effects and higher quality of the selection of seed node comparing with the approaches with traditional IC, LT and CDNF models.