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Efficient Closeness Centrality Computation for Dynamic Graphs

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Database Systems for Advanced Applications (DASFAA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12113))

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Abstract

As a classic metric, closeness centrality can measure the importance of a node in a graph by its proximity to the other nodes. However, exactly calculating closeness centrality of all nodes is significantly time-consuming. Besides, graphs usually evolve with inserted and/or deleted edges, which exacerbates the performance issue if we recompute results from scratch. The paper proposes a preliminary algorithm for calculating exact closeness centrality by using biconnected components and articulation points. Firstly, a large graph is divided into a series of biconnected components which are connected by articulation points. Then distance between arbitrary nodes on the whole graph can be converted into multiple distances between different biconnected components. We further propose an incremental update technique to dynamically maintain the computation results when graphs are changing, like inserting and/or deleting edges, by efficiently detecting all the affected shortest paths to update the closeness centrality based on articulation points. We finally conduct extensive experiments over real graphs to validate the efficiency and effectiveness of our techniques.

Supported by the National Key R&D Program of China (2018YFB1003400), the National Natural Science Foundation of China (61872070, U1811261, 61902366) and Liao Ning Revitalization Talents Program (XLYC1807158).

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Author information

Authors and Affiliations

  1. Northeastern University, Shenyang, 110169, Liaoning, China

    Zhenzhen Shao, Na Guo, Yu Gu, Fangfang Li & Ge Yu

  2. Shenyang Aerospace University, Shenyang, 110136, Liaoning, China

    Na Guo

  3. Ocean University of China, Qingdao, 266100, Shandong, China

    Zhigang Wang

Authors
  1. Zhenzhen Shao
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  2. Na Guo
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  3. Yu Gu
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  4. Zhigang Wang
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  5. Fangfang Li
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  6. Ge Yu
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Corresponding author

Correspondence to Yu Gu .

Editor information

Editors and Affiliations

  1. Dankook University, Yongin, Korea (Republic of)

    Yunmook Nah

  2. Peking University, Haidian, China

    Bin Cui

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Sang-Won Lee

  4. Department of System Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong

    Jeffrey Xu Yu

  5. Kangwon National University, Chunchon, Korea (Republic of)

    Yang-Sae Moon

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Steven Euijong Whang

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Shao, Z., Guo, N., Gu, Y., Wang, Z., Li, F., Yu, G. (2020). Efficient Closeness Centrality Computation for Dynamic Graphs. In: Nah, Y., Cui, B., Lee, SW., Yu, J.X., Moon, YS., Whang, S.E. (eds) Database Systems for Advanced Applications. DASFAA 2020. Lecture Notes in Computer Science(), vol 12113. Springer, Cham. https://doi.org/10.1007/978-3-030-59416-9_32

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  • DOI: https://doi.org/10.1007/978-3-030-59416-9_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59415-2

  • Online ISBN: 978-3-030-59416-9

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