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A multi-view clustering algorithm for attributed weighted multi-edge directed networks

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Abstract

Graph clustering acts as a critical topic for solving decision situations in networks. Different node clustering methods for undirected and directed graphs have been proposed in the literature, but less attention has been paid to the case of attributed weighted multi-edge digraphs (AWMEDiG). Nowadays, multi-source and multi-attributed data are used increasingly in decision sciences; however, traditional methods usually consider single-attributed and single-view data as the input. This type of directed network, whose nodes are described by a list of attributes and directed links are viewed as directed multi-edge, is a new challenge to graph clustering. This paper proposes a new approach to detecting and evaluating clusters of AWMEDiG based on the maximum clique method. Our algorithm first converts the given AWMEDiG into a new weighted graph. This transformation is carried out through a new structural-attributed similarity measurement, an improved version of our previous model. Then, the concept of the maximum clique is adopted to complete the clustering task. The main objective of this new approach is to improve the clustering quality by grouping the highly cohesive and homogeneous vertices. So, the entropy strongly tends to be zero. Moreover, since the number of clusters is not predefined, it has the ability to find out the natural number of clusters within a graph. The performance of the proposed algorithm is tested on a synthetic, provided for a three-view attributed weighted directed network including 50 nodes, and two real-world datasets: UK faculty and Seventh graders. The clustering results are analyzed based on the clusters’ entropy and density, meanwhile the precision and F-score indexes measure the accuracy of them.

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Data availability statement

The synthetic dataset generated and analyzed during the current study are available from the corresponding author on reasonable request. The seventh graders and UK faculty datasets analyzed during the current study are available on http://moreno.ss.uci.edu/data.html and igraphdata of R package, respectively.

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Acknowledgements

This research was supported by the Fundamental Research Grant Schemes, Ref. NO.: FRGS/1/2019/STG06/UPM/02/6, awarded by the Malaysia Ministry of Higher Education (MOHE).

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Authors and Affiliations

  1. Institute for Mathematical Research, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

    Azadeh Zahedi Khameneh, Adem Kilicman & Zahari Mahad

  2. Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

    Adem Kilicman

  3. Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran

    Azadeh Zahedi Khameneh

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  1. Azadeh Zahedi Khameneh
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Correspondence to Azadeh Zahedi Khameneh.

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Khameneh, A.Z., Kilicman, A. & Mahad, Z. A multi-view clustering algorithm for attributed weighted multi-edge directed networks. Neural Comput & Applic 35, 7779–7800 (2023). https://doi.org/10.1007/s00521-022-08086-4

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