Document Type : Original Research Paper


1 Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

2 Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran.


Background and Objectives: Simplicity and flexibility constitute the two basic features for graph models which has made them functional models for real life problems. The attributive graphs are too popular among researchers because of their efficiency and functionality. An attributive graph is a graph the nodes and edges of which can be attributive. Nodes and edges as structural dimension and their attributes as contextual dimension made graphs more flexible in modeling real problems.
Methods: In this study, a new clustering algorithm is proposed based on K-Medoid which focuses on graph’s structure dimension, through heat diffusion algorithm and contextual dimension through weighted Jaccard coefficient in a simultaneous matter. The calculated clusters through proposed algorithm are of denser and nodes with more similar attributes.
Results: DBLP and PBLOG real data sets are applied to evaluate and compare this algorithm with new and well-known cluster algorithms.
Conclusion: Results indicate the outperformers of this algorithm in relation to its counterparts as to structure quality, cluster contextual and time complexity criteria.

©2020 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.


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