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Exploiting behaviors of communities of twitter users for link prediction

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Abstract

Currently, online social networks and social media have become increasingly popular showing an exponential growth. This fact have attracted increasing research interest and, in turn, facilitating the emergence of new interdisciplinary research directions, such as social network analysis. In this scenario, link prediction is one of the most important tasks since it deals with the problem of the existence of a future relation among members in a social network. Previous techniques for link prediction were based on structural (or topological) information. Nevertheless, structural information is not enough to achieve a good performance in the link prediction task on large-scale social networks. Thus, the use of additional information, such as interests or behaviors that nodes have into their communities, may improve the link prediction performance. In this paper, we analyze the viability of using a set of simple and non-expensive techniques that combine structural with community information for predicting the existence of future links in a large-scale online social network, such as Twitter. Twitter, a microblogging service, has emerged as a useful source of informative data shared by millions of users whose relationships require no reciprocation. Twitter network was chosen because it is not well understood, mainly due to the occurrence of directed and asymmetric links yet. Experiments show that our proposals can be used efficiently to improve unsupervised and supervised link prediction task in a directed and asymmetric large-scale network.

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Notes

  1. Ratio of total links per user is the ratio between the total number of links, |E|, and the total number of nodes, |V|. This ratio indicates the average of the size of the neighborhood for each node.

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Acknowledgments

This work is partially supported by Grants 2011/22749-8 from São Paulo Research Foundation (FAPESP) and 151836/2013-2 from National Council for Scientific and Technological Development (CNPq).

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  1. Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Campus de São Carlos, São Carlos, SP, 13560-970, Brazil

    Jorge Valverde-Rebaza & Alneu de Andrade Lopes

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  1. Jorge Valverde-Rebaza
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  2. Alneu de Andrade Lopes
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Correspondence to Jorge Valverde-Rebaza.

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Valverde-Rebaza, J., de Andrade Lopes, A. Exploiting behaviors of communities of twitter users for link prediction. Soc. Netw. Anal. Min. 3, 1063–1074 (2013). https://doi.org/10.1007/s13278-013-0142-8

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