Maximizing the spread of influence through a social network

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target? We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an anal...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: David Kempe, Jon M. Kleinberg, Éva Tardos
• Keywords: Data Mining, KDD 2003, NSF ITR Grant, Provable Approximation Guarantees, Provable Guarantees