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Link sign prediction and ranking in signed directed social networks

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Abstract

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper, we develop a novel approach to handle this situation by applying a new model for node types and use the proposed model to perform link sign prediction and link ranking. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Based upon the proposed features, we present the link sign prediction and link ranking models subsequently. We show that our approaches yield better performance than state-of-the-art approaches for these two tasks based upon three signed directed networks.

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Notes

  1. These datasets are available online at http://snap.stanford.edu/data/.

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Acknowledgments

The work of Dongjin Song and David A. Meyer were supported by the U.S. Department of Defense Minerva Research Initiative/Army under Grant W911NF-09-1-0081 and in part by the National Science Foundation-Division of Mathematical Sciences under Grant 1223137.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, 92093-0409, USA

    Dongjin Song

  2. Department of Mathematics, University of California San Diego, La Jolla, CA, 92093-0112, USA

    David A. Meyer

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  1. Dongjin Song
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  2. David A. Meyer
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Correspondence to Dongjin Song.

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Song, D., Meyer, D.A. Link sign prediction and ranking in signed directed social networks. Soc. Netw. Anal. Min. 5, 52 (2015). https://doi.org/10.1007/s13278-015-0288-7

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