document: Scalable influence maximization for prevalent viral marketing in large-scale social networks Influence maximization , defined by Kempe , Kleinberg , and Tardos ( 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 viral marketing in large-scale online social networks . Prior solutions , such as the greedy algorithm of Kempe et al. ( 2003 ) and its improvements are slow and not scalable , while other heuristic algorithms do not provide consistently good performance on influence spreads . In this paper , we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments . Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm . Our results from extensive simulations on several real-world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem : ( a ) our algorithm scales beyond million-sized graphs where the greedy algorithm becomes infeasible , and ( b ) in all size ranges , our algorithm performs consistently well in influence spread -- it is always among the best algorithms , and in most cases it significantly outperforms all other scalable heuristics to as much as 100 % -- 260 % increase in influence spread .
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