Maximizing the Spread of Influence Through a Social Network

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Maximizing the Spread of Influence Through a Social Network (2003)

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by David Kempe

Venue:	In KDD
Citations:	989 - 7 self

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@INPROCEEDINGS{Kempe03maximizingthe,
    author = {David Kempe},
    title = {Maximizing the Spread of Influence Through a Social Network},
    booktitle = {In KDD},
    year = {2003},
    pages = {137--146},
    publisher = {ACM Press}
}

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Abstract

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of “word of mouth ” in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target? We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63 % of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks. We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform nodeselection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

Keyphrases

social network new product large cascade technological innovation fundamental algorithmic problem several class large collaboration network degree centrality distance centrality computational experiment efficient algorithm game-theoretic setting general approach out-perform nodeselection heuristic widespread adoption natural greedy strategy social network process social network analysis optimization problem well-studied notion performance guarantee analysis framework first provable approximation guarantee influence propagate submodular function provable guarantee influential node various strategy influence problem viral marketing strategy

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