Abstract
Community structure is one of the most important properties of complex networks and a keypoint to understanding and exploring real-world networks. One popular technique for community detection is matrix-based algorithms. However, existing matrix-based community detection models, such as nonnegative matrix factorization, spectral clustering and their variants, fit the data in a Euclidean space and have ignored the local consistency information which is crucial when discovering communities. In this paper, we propose a novel framework of latent space clustering to cope with community detection, by incorporating the clique-based locally consistency into the original objective functions to penalize the latent space dissimilarity of the nodes within the clique. We evaluate the proposed methods on both synthetic and real-world networks and experimental results show that our approaches significantly improve the accuracy of community detection and outperform state-of-the-art methods, especially on networks with unclear structures.
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Acknowledgments
This work is supported by the National High Technology Research and Development Program (863 Program) of China (2014AA015104), the National Natural Science Foundation of China (61402002, 61272152 and 61472002), the Natural Science Foundation of Anhui Province (1408085QF120), the Natural Science Foundation of Anhui Higher Education Institutions (KJ2016A040), Open Project of IAT Collaborative Innovation Center of Anhui University (ADXXBZ201511) and Public Sentiment and Regional Image Research Center of Anhui University (Y01002364).
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Ding, Z., Sun, D., Zhang, X., Luo, B. (2016). Clique-Based Locally Consistent Latent Space Clustering for Community Detection. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_55
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DOI: https://doi.org/10.1007/978-981-10-3002-4_55
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