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CELF++: optimizing the greedy algorithm for influence maximization in social networks

CELF++: optimizing the greedy algorithm for influence maximization in social networks,10.1145/1963192.1963217,Amit Goyal,Wei Lu,Laks V. S. Lakshmanan

CELF++: optimizing the greedy algorithm for influence maximization in social networks  
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Kempe et al. [4] (KKT) showed the problem of influence maximization is NP-hard and a simple greedy algorithm guarantees the best possible approximation factor in PTIME. However, it has two major sources of inefficiency. First, finding the expected spread of a node set is #P-hard. Second, the basic greedy algorithm is quadratic in the number of nodes. The first source is tackled by estimating the spread using Monte Carlo simulation or by using heuristics[4, 6, 2, 5, 1, 3]. Leskovec et al. proposed the CELF algorithm for tackling the second. In this work, we propose CELF++ and empirically show that it is 35-55% faster than CELF.
Conference: World Wide Web Conference Series - WWW , pp. 47-48, 2011
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