Maximizing Social Influence in Nearly Optimal Time

Christian Borgs; Michael Brautbar; Jennifer Chayes; Brendan Lucier

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arXiv · 1212.0884

Maximizing Social Influence in Nearly Optimal Time

Christian Borgs·Michael Brautbar·Jennifer Chayes·Brendan Lucier

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Abstract

Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Runtime is a primary consideration for this problem due to the massive size of the relevant input networks. We provide a fast algorithm for the influence maximization problem, obtaining the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon > 0, in time O((m+n)k log(n) / epsilon^2). Our algorithm is runtime-optimal (up to a logarithmic factor) and substantially improves upon the previously best-known algorithms which run in time Omega(mnk POLY(1/epsilon)). Furthermore, our algorithm can be modified to allow early termination: if it is terminated after O(beta(m+n)k log(n)) steps for some beta < 1 (which can depend on n), then it returns a solution with approximation factor O(beta). Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.

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BibTeX RIS

Christian Borgs, Michael Brautbar, Jennifer Chayes, Brendan Lucier. 2012-12-04. Maximizing Social Influence in Nearly Optimal Time. https://arxiv.org/abs/1212.0884

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