AAAI Publications, Thirty-Second AAAI Conference on Artificial Intelligence

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Lagrangian Constrained Community Detection
Mohadeseh Ganji, James Bailey, Peter J. Stuckey

Last modified: 2018-04-29

Abstract


Semi-supervised or constrained community detection incorporates side information to findcommunities of interest in complex networks. The supervision is often represented as constraints such as known labels and pairwise constraints. Existing constrained community detection approaches often fail to fully benefit from the available side information. This results in poor performance for scenarios such as: when the constraints are required to be fully satisfied, when there is a high confidence about the correctness of the supervision information, and in situations where the side information is expensive or hard to achieve and is only available in a limited amount. In this paper, we propose a new constrained community detection algorithm based on Lagrangian multipliers to incorporate and fully satisfy the instance level supervisio nconstraints. Our proposed algorithm can more fully utilise available side information and find better quality solutions. Our experiments on real and synthetic data sets show our proposed LagCCD algorithm outperforms existing algorithms in terms of solution quality, ability to satisfy the constraints and noise resistance.

Keywords


Semi-supervised Learning; Community Detection, Lagrange Multipliers Method

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