Abstract
Finding components with high connectivity is an important problem in component detection with a wide range of applications, e.g., social network analysis, web-page research and bioinformatics. In particular, k-edge connected component (k-ECC) has recently been extensively studied to discover disjoint components. Yet many real applications present needs and challenges for overlapping components. In this paper, we propose a k-vertex connected component (k-VCC) model, which is much more cohesive and therefore allows overlapping between components. To find k-VCCs, a top-down framework is first developed to find the exact k-VCCs. To further reduce the high computational cost for input networks of large sizes, a bottom-up framework is then proposed. Instead of using the structure of the entire network, it locally identifies the seed subgraphs, and obtains the heuristic k-VCCs by expanding and merging these seed subgraphs. Comprehensive experimental results on large real and synthetic networks demonstrate the efficiency and effectiveness of our approaches.
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Acknowledgments
This research is partially supported by the National NSFC (No. 61272182, 61100028, 61332014, U1401256, 61672144), the Fundamental Research Funds for the Central Universities (N150402002, N150404008), the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative and the Pinnacle lab for Analytics at SMU.
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Li, Y., Zhao, Y., Wang, G., Zhu, F., Wu, Y., Shi, S. (2017). Effective k-Vertex Connected Component Detection in Large-Scale Networks. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_25
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DOI: https://doi.org/10.1007/978-3-319-55699-4_25
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