Scalable influence maximization for independent cascade model in large-scale social networks

• Published in: Data mining and knowledge discovery Vol. 25; no. 3; pp. 545 - 576
• Main Authors: Wang, Chi, Chen, Wei, Wang, Yajun
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.11.2012; Springer Nature B.V
• Subjects: Algorithms; Approximation; Artificial Intelligence; Cascades; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Graphs; Greedy algorithms; Heuristic; Heuristic methods; Information Storage and Retrieval; Mathematical models; Maximization; Physics; Seeds; Social networks; Spreads; Statistics for Engineering; Viral marketing; Word of mouth advertising; Influence maximization; Social networks; Viral marketing; Independent cascade model

Influence maximization, defined by Kempe et al. (SIGKDD 2003 ), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent vira...