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- W2293954148 abstract "We consider the problem of finding a set of k vertices of maximum total influence in a given undirected network, under the independent cascade (IC) model of influence spread. It is known that influence is monotone and submodular in the IC model, and hence a greedy algorithm achieves a (1--1/e) approximation to this problem. Moreover, it is known to be NP-hard to achieve a better approximation factor in directed networks.We show that for undirected networks, this approximation barrier can be overcome: the greedy algorithm obtains an (1--1/e + c) approximation to the set of optimal influence, for some constant c > 0. Our proof proceeds via probabilistic analysis of bond percolation in arbitrary finite networks. We also show that the influence maximization problems remains APX-hard in undirected networks." @default.
- W2293954148 created "2016-06-24" @default.
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- W2293954148 date "2014-01-05" @default.
- W2293954148 modified "2023-09-23" @default.
- W2293954148 title "Influence maximization in undirected networks" @default.
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- W2293954148 doi "https://doi.org/10.5555/2634074.2634183" @default.
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