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Modeling and measuring graph similarity: the case for centrality distance

Published: 11 August 2014 Publication History

Abstract

The study of the topological structure of complex networks has fascinated researchers for several decades, and today we have a fairly good understanding of the types and reoccurring characteristics of many different complex networks. However, surprisingly little is known today about models to compare complex graphs, and quantitatively measure their similarity. This paper proposes a natural similarity measure for complex networks: centrality distance, the difference between two graphs with respect to a given node centrality. Centrality distances allow to take into account the specific roles of the different nodes in the network, and have many interesting applications. As a case study, we consider the closeness centrality in more detail, and show that closeness centrality distance can be used to effectively distinguish between randomly generated and actual evolutionary paths of two dynamic social networks.

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    cover image ACM Conferences
    FOMC '14: Proceedings of the 10th ACM international workshop on Foundations of mobile computing
    August 2014
    66 pages
    ISBN:9781450329842
    DOI:10.1145/2634274
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    Published: 11 August 2014

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    Author Tags

    1. centrality
    2. complex networks
    3. dynamics
    4. graph similarity
    5. link prediction

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    FOMC '14 Paper Acceptance Rate 7 of 8 submissions, 88%;
    Overall Acceptance Rate 7 of 8 submissions, 88%

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    • (2023)Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritizationStatistical Applications in Genetics and Molecular Biology10.1515/sagmb-2022-005422:1Online publication date: 21-Nov-2023
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