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TSS: Temporal similarity search measure for heterogeneous information networks

Author

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  • Nikmehr, Golnaz
  • Salehi, Mostafa
  • Jalili, Mahdi

Abstract

Many real-world phenomena can be modeled as networked systems. Some of these systems consist of heterogeneous nodes and edges. Similarity search is a fundamental operation in network systems, which is a basis for various applications such as link prediction and recommendation. This manuscript introduces a temporal similarity measure for heterogeneous networks. The proposed metric is based on metapath strategy and considers time of the interaction. We provide detailed properties of the proposed temporal similarity measures. Our experimental results on a number of real heterogeneous social networks show that incorporating time in the computation of the similarity significantly improves the performance as compared to the state-of-the-art similarity computation methods.

Suggested Citation

  • Nikmehr, Golnaz & Salehi, Mostafa & Jalili, Mahdi, 2019. "TSS: Temporal similarity search measure for heterogeneous information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 696-707.
    • Handle: RePEc:eee:phsmap:v:524:y:2019:i:c:p:696-707
    DOI: 10.1016/j.physa.2019.04.207
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    Cited by:

    1. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).

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