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On detecting maximal quasi antagonistic communities in signed graphs

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Abstract

Many networks can be modeled as signed graphs. These include social networks, and relationships/interactions networks. Detecting sub-structures in such networks helps us understand user behavior, predict links, and recommend products. In this paper, we detect dense sub-structures from a signed graph, called quasi antagonistic communities (QACs). An antagonistic community consists of two groups of users expressing positive relationships within each group but negative relationships across groups. Instead of requiring complete set of negative links across its groups, a QAC allows a small number of inter-group negative links to be missing. We propose an algorithm, Mascot, to find all maximal quasi antagonistic communities (MQACs). Mascot consists of two stages: pruning and enumeration stages. Based on the properties of QAC, we propose four pruning rules to reduce the size of candidate graphs in the pruning stage. We use an enumeration tree to enumerate all strongly connected subgraphs in a top–down fashion in the second stage before they are used to construct MQACs. We have conducted extensive experiments using synthetic signed graphs and two real networks to demonstrate the efficiency and accuracy of the Mascot algorithm. We have also found that detecting MQACs helps us to predict the signs of links.

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Notes

  1. Justin Bieber and Conor Maynard are two teens who enjoy wide success in their singing career.

  2. Another version of absolute QB that does not include condition (2) was proposed in a subsequent work (Sim et al. 2006).

  3. Connectivity of any pair of vertices in a \((\epsilon , min\_size)\) absolute QB or \((\delta , min\_size)\) relative QB can be proven in a similar manner.

  4. http://snap.stanford.edu/data/soc-sign-epinions.html

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Acknowledgments

This work is supported by the National Basic Research Program (973) of China (No. 2012CB316203) and NSFC under Grant Nos. 61033007, 61103039, 61402177 and 61232002. This work is also supported by the National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, and National Research Foundation (NRF) (NRF2008IDM-IDM004-036). We are grateful to the anonymous reviewers for their valuable and insightful comments. We would also like to thank BuzzCity Pte Ltd for sharing their data set with us.

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Authors and Affiliations

  1. Institute for Data Science and Engineering, East China Normal University, Shanghai, China

    Ming Gao

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ming Gao, Ee-Peng Lim, David Lo & Philips Kokoh Prasetyo

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  1. Ming Gao
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  2. Ee-Peng Lim
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  3. David Lo
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  4. Philips Kokoh Prasetyo
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Correspondence to Ming Gao.

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Responsible editor: Charu Aggarwal.

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Gao, M., Lim, EP., Lo, D. et al. On detecting maximal quasi antagonistic communities in signed graphs. Data Min Knowl Disc 30, 99–146 (2016). https://doi.org/10.1007/s10618-015-0405-2

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