{"ID":2876453,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02609","arxiv_id":"2509.02609","title":"Contrastive clustering based on regular equivalence for influential node identification in complex networks","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.","short_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-...","url_abs":"https://arxiv.org/abs/2509.02609","url_pdf":"https://arxiv.org/pdf/2509.02609v1","authors":"[\"Yanmei Hu\",\"Yihang Wu\",\"Bing Sun\",\"Xue Yue\",\"Biao Cai\",\"Xiangtao Li\",\"Yang Chen\"]","published":"2025-08-30T09:34:39Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.AI\"]","methods":"[]","has_code":false}