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Durable relationship prediction and description using a large dynamic graph

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Abstract

Dynamic graphs are a data structure widely used in representing changeable relationships or connections between different entities. This paper proposes a novel type of node similarity, based on the frequency of connections between nodes to describe the changeable relationships between entities over a period; this has not been considered before as an indication of similarity between two nodes. In other words, if two entities have a history of frequent connections, this means that they have something in common and have a durable relationship. In this paper, durable relationships describe the frequency of connections rather than only the continuous connection between two nodes. Thus, durable relationships are defined in two dimensions: (i) In the dimension of time, they can be categorized based on the length of duration as short-term, medium-term, or long-term relationships; (ii) Based on frequencies of connections over a period, they can be categorized into four statuses (No Relationship, Weak Relationship, In Relationship, and Strong Relationship). Based on this definition of durable relationships, a node similarity measurement algorithm is proposed, to study the status of relationships from a longitudinal study point of view. This method provides a new way to describe the semantics of relationships (such as collaborative relationships, or customer loyalty descriptions) and also gives a practical application of node similarity measurement in a real world, which is to provide a prediction of relationship. Our extensive experiments have shown that the proposed method can effectively describe durable relationships and especially predict future relationships.

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Acknowledgements

This work is supported in part by the New Zealand Marsden Fund, the Chinese Scholarship Council, and the National Natural Science Foundation of China under Grant (No.61472169).

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Authors and Affiliations

  1. Institute of Natural and Mathematical Sciences, Massey University, Auckland, 0632, New Zealand

    Ruili Wang & Wanting Ji

  2. School of Information, Liaoning University, Shenyang, 110036, China

    Baoyan Song

Authors
  1. Ruili Wang
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  2. Wanting Ji
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  3. Baoyan Song
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Corresponding author

Correspondence to Ruili Wang.

Additional information

This article belongs to the Topical Collection: Special Issue on Deep Mining Big Social Data

Guest Editors: Xiaofeng Zhu, Gerard Sanroma, Jilian Zhang, and Brent C. Munsell

Appendix

Appendix

Table 8 The list of the subject node v 19 and its subject-related nodes
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Wang, R., Ji, W. & Song, B. Durable relationship prediction and description using a large dynamic graph. World Wide Web 21, 1575–1600 (2018). https://doi.org/10.1007/s11280-017-0510-9

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  • DOI: https://doi.org/10.1007/s11280-017-0510-9

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