Trust based latency aware influence maximization in social networks. (May 2015)

• Record Type: Journal Article
• Authors: Mohamadi-Baghmolaei, Rezvan; Mozafari, Niloofar; Hamzeh, Ali
• Is Part Of: Engineering applications of artificial intelligence. Volume 41(2015:May)
• Page Start: 195
• Page End: 206
• Publication Date: 2015-05
• Subjects: Influence maximization -- Information diffusion -- Independent cascade model -- Trust path -- Distrust path -- TLIM; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.02.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. They have mainly relied on similar temporal chances for every node to influence another; whereas in reality, time plays a major role in pairwise propagation rates in social networks. However, there is little research done on influence maximization considering temporal dynamics of the networks and existing approaches merely offers a mediocre performance due to ignoring trust aspects of the diffusion process. In this paper, we propose a Trust based Latency aware Influence Maximization model, abbreviated as TLIM, which selects the most influential nodes in social networks with considering time and trust simultaneously. To the best of our knowledge, we are the first to study trust in classic IC model and also the first to consider both important time and trust factors jointly in influence maximization problem. The main contributions of this paper are listed as follows: first, we extend the classic IC model to include time and trust simultaneously, which is more applicable in existing social networks. Second, we find the most influential nodes in social networks with considering time and trust together; and the last but not the least, it is applicable to well-known real social networks such as Epinions, Slashdot and Wikipedia. To explore the advantages of our approach, quite a lot of experiments with different specifications are conducted. The obtained results are very promising.