A Passive Clustering-Based Approach for Important Node Mining in Multi-Relational Networks
DOI:
https://doi.org/10.15837/ijccc.2026.2.7108Keywords:
Multi-relational networks, centrality measures, important node identification, passive clustering, entity nodesAbstract
The identification of key nodes within multi-relational networks presents significant challenges due to the heterogeneity of node attributes and the varying significance of different attributes across distinct relationships. Conventional methods often fail to effectively capture these complexities, leading to suboptimal mining outcomes. To address this issue, a passive clustering-based approach is introduced to enhance the identification of important nodes in multi-relational networks. By constructing an adjacency matrix framework, the network structure is systematically represented, encapsulating the connectivity relationships among nodes. The comprehensive centrality of entity nodes is then evaluated to preliminarily select candidates with substantial network influence. Subsequently, a passive clustering algorithm is applied to categorize nodes into clusters based on attribute similarities, enabling a refined analysis within each cluster. The principle of node centrality metrics is further adapted to assess node importance within and across clusters, thereby mitigating the impact of attribute heterogeneity. Nodes exhibiting weak intra-cluster associations are eliminated, ensuring the robustness of the clustering process. The proposed method demonstrates superior efficiency and scalability, requiring a memory footprint below 160 KB. Furthermore, the computational efficiency of node degree centrality, median centrality, and proximity centrality is improved, with relative computational time ratios of 14.2%, 8.9%, and 8.6%, respectively. These results indicate that the proposed approach effectively captures complex dynamic interactions within undirected and unprivileged multi-relational networks, offering a scalable and computationally efficient solution for important node mining.
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