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Node clustering in complex networks based on structural similarity

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  • Feng, Deyue
  • Li, Meizhu
  • Zhang, Qi

Abstract

In the structural analysis of complex networks, node clustering is a method that identifying nodes with the same function or structural properties, which is also a part of the community detection. In this work, based on the structural similarity of nodes and the k-means++ algorithm, a new method of node clustering is proposed. This method can easily divide the hub nodes and peripheral nodes in the network with a core-peripheral structure into two sets. We also find that the changes in the number of nodes in different classes is a manifestation of the rules that guide the growth of the network under different conditions. Specifically, when the network’s growth follows the Erdős–Rényi model, the cluster of nodes is homogeneous. When the network’s growth adheres to the Barabási–Albert model, the peripheral nodes are the majority, and this trend will not change with the growing network size. All the results show that the clustering of nodes in the networks based on the nodes’ structural similarity can be used as a new method for research on the structural analysis of complex networks.

Suggested Citation

  • Feng, Deyue & Li, Meizhu & Zhang, Qi, 2025. "Node clustering in complex networks based on structural similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 658(C).
    • Handle: RePEc:eee:phsmap:v:658:y:2025:i:c:s0378437124007842
    DOI: 10.1016/j.physa.2024.130274
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