Propagation of trust and distrust

Guha, Ramanathan V.; Kumar, Raman; Raghavan, Prabhakar; Tomkins, Andrew

doi:10.1145/988672.988727

Cited by 1,162 publications

(866 citation statements)

References 24 publications

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“…For example, a social network may consist of positive and negative links, respectively pointing to friends and foes [153], or trusted and distrusted peers [154]. Leskovec et al [155] proposed a method to predict the signs of links (positive or negative), yet the prediction of both the existence of a link and its sign has not been well studied.…”

Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,383

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Discussionmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,383

1,621

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“…With respect to diffusion in social networks, researchers have studied the propagation of favorite photographs in a Flickr network [6], the spread of information [16,23] via Internet communication, and the effects of online purchase recommendations [26], to name a few. In some instances, models of diffusion are combined with data mining to predict social phenomena (e.g., product marketing [9,31] and trust propagation [17]). …”

Section: Introductionmentioning

confidence: 99%

Finding Critical Nodes for Inhibiting Diffusion of Complex Contagions in Social Networks

Kuhlman

Kumar

Marathe

et al. 2010

Machine Learning and Knowledge Discovery in Databases

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Abstract.We study the problem of inhibiting diffusion of complex contagions such as rumors, undesirable fads and mob behavior in social networks by removing a small number of nodes (called critical nodes) from the network. We show that, in general, for any ρ ≥ 1, even obtaining a ρ-approximate solution to these problems is NP-hard. We develop efficient heuristics for these problems and carry out an empirical study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that the heuristics perform well on the three social networks.

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“…Third, the repository incorporates direct trust and transitive trust [12,13] to obtain the reporter trust value for i: rt i . It does so by analyzing the reporter trust graph T (V, E) from the point of view of a small set of pre-trusted nodes in V. These pre-trusted nodes are administered by competent admins that are fully trusted by the SocialFilter repository.…”

Section: Reporter Trustmentioning

confidence: 99%