2010 FindingEffectorsinSocialNetwork

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Social Networks, information propagation, graph algorithms.

Abstract

Assume a network (V,E) where a subset of the nodes in V are active. We consider the problem of selecting a set of [math]\displaystyle{ k }[/math] active nodes that best explain the observed activation state, under a given information-propagation model. We call these nodes effectors. We formally define the [math]\displaystyle{ k }[/math]-Effectors problem and study its complexity for different types of graphs. We show that for arbitrary graphs the problem is not only NP-hard to solve optimally, but also NP-hard to approximate. We also show that, for some special cases, the problem can be solved optimally in polynomial time using a dynamic-programming algorithm. To the best of our knowledge, this is the first work to consider the [math]\displaystyle{ k }[/math]-Effectors problem in networks. We experimentally evaluate our algorithms using the DBLP co-authorship graph, where we search for effectors of topics that appear in research papers.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 FindingEffectorsinSocialNetworkHeikki Mannila
Dimitrios Gunopulos
Theodoros Lappas
Evimaria Terzi
Finding Effectors in Social NetworksKDD-2010 Proceedingshttp://cs-people.bu.edu/evimaria/papers/rp262b-lappas.pdf10.1145/1835804.18359372010