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Random Walk and Shared Neighbors-Based Similarity for Patterns in Graph Data

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 88))

Abstract

This paper discusses the similarity of patterns in graph data. Here, the so-named graph data is composed by two absolutely different information, one is pattern’s attribute information and the other is the relation between patterns, i.e., topological information. Bearing this in mind, a random walk and shared neighbors-based similarity measurement for patterns is proposed. In this measurement, the reachability of any two patterns is fully studied. For the patterns topological structure is connected, we consider the similarity of shared neighbors and topological information. For patterns, whose topological structure is unconnected, we only consider the attribute-based information of patterns. On this base, a composite and novel measurement similarity for patterns in graph data is constructed. In conclusion, an example is given to verify the effectiveness of the measurement.

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Acknowledgement

This work is supported by the National Natural Science Foundation of China (Nos. 61966039, 61866040, 11971065), Yunnan Province education department scientific research fund project (Nos. 2021Y670).

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Authors and Affiliations

  1. School of Mathematics and Computer Sciences, Yunnan Minzu University, Kunming, 650504, China

    Li Deng & Shihu Liu

  2. School of Education, Yunnan Minzu University, Kunming, 650504, China

    Guoding Duan

Authors
  1. Li Deng
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  2. Shihu Liu
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  3. Guoding Duan
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Editor information

Editors and Affiliations

  1. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Hongying Meng

  2. School of Electronical Information and Artificial Engineering, Shaanxi University of Science and Technology, Xi’an, China

    Tao Lei

  3. College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, UK

    Maozhen Li

  4. College of Electrical and Information, Hunan University, Changsha, China

    Kenli Li

  5. Division of Intelligent Future Technologies, Mälardalen University, Västerås, Västmanlands Län, Sweden

    Ning Xiong

  6. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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Deng, L., Liu, S., Duan, G. (2021). Random Walk and Shared Neighbors-Based Similarity for Patterns in Graph Data. In: Meng, H., Lei, T., Li, M., Li, K., Xiong, N., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 88. Springer, Cham. https://doi.org/10.1007/978-3-030-70665-4_141

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