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. 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. The two conference papers upon which this article is based (KDD 2003 and ICALP 2005) provide the first provable approximation guarantees for efficient algorithms. Using an The present article is an expanded version of two conference papers [51, 52], which appeared in KDD 2003 and ICALP 2005, respectively. ∗Supported in part by an Intel Graduate Fellowship and an NSF Graduate Research Fellowship, an NSF CAREER Award, an ONR Young Investigator Award, and a Sloan Fellowship. †Supported in part by a David and Lucile Packard Foundation Fellowship and NSF ITR/IM Grant IIS-0081334. ‡Supported in part by NSF ITR grant CCR-011337, and ONR grant N00014-98-1-0589. ACM Classification: F.2.2, G.3 AMS Classification: 68W25, 90C59, 68Q25, 68Q17

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