Maximizing the Spread of Influence through a Social Network
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The problem of finding the most influential nodes in a social network is NP-hard as mentioned in this paper, and the first provable approximation guarantees for efficient algorithms were provided by Domingos et al. using an analysis framework based on submodular functions.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 node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.read more
Citations
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Book ChapterDOI
Submodular Function Maximization
Andreas Krause,Daniel Golovin +1 more
TL;DR: This survey will introduce submodularity and some of its generalizations, illustrate how it arises in various applications, and discuss algorithms for optimizing submodular functions.
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Information diffusion in online social networks: a survey
TL;DR: A survey of representative methods dealing with information diffusion in social networks and a taxonomy that summarizes the state-of-the-art is proposed, intended to help researchers in quickly understanding existing works and possible improvements to bring.
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Data Mining: The Textbook
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Proceedings ArticleDOI
Influence Maximization in Near-Linear Time: A Martingale Approach
TL;DR: The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
Journal ArticleDOI
The Majority Illusion in Social Networks
TL;DR: A statistical model is developed that quantifies the effect of the majority illusion and shows that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure.
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