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Measuring in-network node similarity based on neighborhoods: a unified parametric approach

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Abstract

In many applications, we need to measure similarity between nodes in a large network based on features of their neighborhoods. Although in-network node similarity based on proximity has been well investigated, surprisingly, measuring in-network node similarity based on neighborhoods remains a largely untouched problem in literature. One challenge is that in different applications we may need different measurements that manifest different meanings of similarity. Furthermore, we often want to make trade-offs between specificity of neighborhood matching and efficiency. In this paper, we investigate the problem in a principled and systematic manner. We develop a unified parametric model and a series of four instance measures. Those instance similarity measures not only address a spectrum of various meanings of similarity, but also present a series of trade-offs between computational cost and strictness of matching between neighborhoods of nodes being compared. By extensive experiments and case studies, we demonstrate the effectiveness of the proposed model and its instances.

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Notes

  1. http://www.iam.unibe.ch/fki/databases/iam-graph-database.

  2. The code is available at http://web.eecs.umich.edu/ dkoutra/CODE/fabp.zip(FaBP) [17]. Since FaBP is for binary classification and generates a belief of being positive for every node, we ran FaBP for each label in the dataset and the label of an unlabeled node is the label that has the highest belief value.

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Authors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Yu Yang & Jian Pei

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Abdullah Al-Barakati

Authors
  1. Yu Yang
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  2. Jian Pei
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  3. Abdullah Al-Barakati
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Correspondence to Jian Pei.

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Yang, Y., Pei, J. & Al-Barakati, A. Measuring in-network node similarity based on neighborhoods: a unified parametric approach. Knowl Inf Syst 53, 43–70 (2017). https://doi.org/10.1007/s10115-017-1033-5

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  • DOI: https://doi.org/10.1007/s10115-017-1033-5

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