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Journal of the ACM (JACM)
Bipartite Subgraphs and the Smallest Eigenvalue
Combinatorics, Probability and Computing
Max cut and the smallest eigenvalue
Proceedings of the forty-first annual ACM symposium on Theory of computing
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
International Journal of Wireless and Mobile Computing
Privacy analysis in mobile social networks: the influential factors for disclosure of personal data
International Journal of Wireless and Mobile Computing
On routing protocols using mobile social networks
International Journal of Wireless and Mobile Computing
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Many networks of interest in sciences and social research can be divided naturally into anti-communities. The problem of detecting and characterising such anti-community structure has attracted recent attention. In this paper, we first define the anti-modularity as a quantitative measure over the possible partitioning of a network. We also show that the anti-modularity can be reformulated in terms of the eigenvectors of a characteristic matrix for the network, which we call the anti-modularity matrix. Based on the anti-modularity matrix, a spectral-based algorithm for anti-community detection is proposed. We also prove that the anti-modularity matrix is identical to the covariance matrix of the column vectors in the adjacent matrix ignoring a constant factor, and our algorithm essentially accomplishes a principal component analysis on the adjacent matrix. Experimental results on synthetic and real networks show that the anti-modularity is reliable as a measurement for the anti-community partitioning, and our algorithm can effectively detect the anti-communities.