Abstract
The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of \(\mathsf {discriminative}\) \(\mathsf {core}\), which automatically models various kinds of social contexts without parameter t. Leveraging on \(\mathsf {discriminative}\) \(\mathsf {cores}\) and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.
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Acknowledgments
This work is supported by the NSFC Nos. 61702435, 61972291, RGC Nos. 12200917, 12200817, CRF C6030-18GF, and the National Science Foundation of Hubei Province No. 2018CFB519.
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Huang, J., Huang, X., Zhu, Y., Xu, J. (2019). Parameter-Free Structural Diversity Search. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_43
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