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International Conference on Web and Internet Economics

WINE 2017: Web and Internet Economics pp 16–29Cite as

Don’t Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10660)

Abstract

We consider the problem of maximizing the spread of influence in a social network by choosing a fixed number of initial seeds — a central problem in the study of network cascades. The majority of existing work on this problem, formally referred to as the influence maximization problem, is designed for submodular cascades. Despite the empirical evidence that many cascades are non-submodular, little work has been done focusing on non-submodular influence maximization.

We propose a new heuristic for solving the influence maximization problem and show via simulations on real-world and synthetic networks that our algorithm outputs more influential seed sets than the state-of-the-art greedy algorithm in many natural cases, with average improvements of 7% for submodular cascades, and 55% for non-submodular cascades. Our heuristic uses a dynamic programming approach on a hierarchical decomposition of the social network to leverage the relation between the spread of cascades and the community structure of social networks. We present “worst-case” theoretical results proving that in certain settings our algorithm outputs seed sets that are a factor of \(\varTheta (\sqrt{n})\) more influential than those of the greedy algorithm, where n is the number of nodes in the network.

The full version is located at https://arxiv.org/abs/1609.06520.

The authors gratefully acknowledge the support of the National Science Foundation under Career Award 1452915 and AifT Award 1535912.

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Notes

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    See the full version for all of these results at https://arxiv.org/abs/1609.06520.

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Authors and Affiliations

  1. University of Massachusetts, Amherst, USA

    Rico Angell

  2. University of Michigan, Ann Arbor, USA

    Grant Schoenebeck

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  1. Rico Angell
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Correspondence to Rico Angell .

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  1. Microsoft Research, Redmond, Washington, USA

    Nikhil R. Devanur

  2. Shanghai University of Finance and Economics, Shanghai, China

    Pinyan Lu

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Angell, R., Schoenebeck, G. (2017). Don’t Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization. In: R. Devanur, N., Lu, P. (eds) Web and Internet Economics. WINE 2017. Lecture Notes in Computer Science(), vol 10660. Springer, Cham. https://doi.org/10.1007/978-3-319-71924-5_2

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  • DOI: https://doi.org/10.1007/978-3-319-71924-5_2

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