Contrastive clustering based on regular equivalence for influential node identification in complex networks

• URL: http://arxiv.org/abs/2509.02609v1
• Date: Sat, 30 Aug 2025 09:34:39 GMT
• Title: Contrastive clustering based on regular equivalence for influential node identification in complex networks
• Authors: Yanmei Hu, Yihang Wu, Bing Sun, Xue Yue, Biao Cai, Xiangtao Li, Yang Chen,
• Abstract summary: ReCC is a novel deep unsupervised framework for influential node identification.<n>It is pre-trained using network reconstruction loss and fine-tuned with a combined contrastive and clustering loss.<n>Extensive experiments demonstrate that ReCC outperforms state-of-the-art approaches across several benchmarks.
• Score: 10.538045764554019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying influential nodes in complex networks is a fundamental task in network analysis with wide-ranging applications across domains. While deep learning has advanced node influence detection, existing supervised approaches remain constrained by their reliance on labeled data, limiting their applicability in real-world scenarios where labels are scarce or unavailable. While contrastive learning demonstrates significant potential for performance enhancement, existing approaches predominantly rely on multiple-embedding generation to construct positive/negative sample pairs. To overcome these limitations, we propose ReCC (\textit{r}egular \textit{e}quivalence-based \textit{c}ontrastive \textit{c}lustering), a novel deep unsupervised framework for influential node identification. We first reformalize influential node identification as a label-free deep clustering problem, then develop a contrastive learning mechanism that leverages regular equivalence-based similarity, which captures structural similarities between nodes beyond local neighborhoods, to generate positive and negative samples. This mechanism is integrated into a graph convolutional network to learn node embeddings that are used to differentiate influential from non-influential nodes. ReCC is pre-trained using network reconstruction loss and fine-tuned with a combined contrastive and clustering loss, with both phases being independent of labeled data. Additionally, ReCC enhances node representations by combining structural metrics with regular equivalence-based similarities. Extensive experiments demonstrate that ReCC outperforms state-of-the-art approaches across several benchmarks.

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