Title

Effective K-Vertex connected component detection in large-scale networks

Publication Type

Conference Proceeding Article

Publication Date

3-2017

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.

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Management and Analytics

Publication

Proceedings of The 22nd International Conference on Database Systems for Advanced Applications: Suzhou, China, 2017 March 27-30

Volume

10178

First Page

404

Last Page

421

ISBN

9783319556994

Identifier

10.1007/978-3-319-55699-4_25

City or Country

Suzhou, China

Additional URL

http://doi.org./10.1007/978-3-319-55699-4_25

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