{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/less-is-more-simple-yet-effective-heuristic","title":"Less is More: Simple yet Effective Heuristic Community Detection with Graph Convolution Network","arxiv_id":"2501.12946","date":"2025-01-22","proceeding":null,"authors":["Hong Wang","Yinglong Zhang","Zhangqi Zhao","Zhicong Cai","Xuewen Xia","Xing Xu"],"abstract":"Community detection is crucial in data mining. Traditional methods primarily focus on graph structure, often neglecting the significance of attribute features. In contrast, deep learning-based approaches incorporate attribute features and local structural information through contrastive learning, improving detection performance. However, existing algorithms' complex design and joint optimization make them difficult to train and reduce detection efficiency. Additionally, these methods require the number of communities to be predefined, making the results susceptible to artificial interference. To address these challenges, we propose a simple yet effective community detection algorithm that can adaptively detect communities without relying on data augmentation and contrastive optimization. The proposed algorithm first performs community pre-detection to extract global structural information adaptively. It then utilizes GCN to integrate local structures and attribute features. Subsequently, it combines global, local structures and attribute features in the feature space to discover community affiliations. Finally, a modularity maximization method is employed to optimize the communities based on these three types of information, thereby uncovering the community affiliation of each node. We conduct experimental comparisons across various graph datasets, evaluating the proposed algorithm against traditional methods and state-of-the-art community detection algorithms. The experimental results demonstrate that our algorithm achieves greater efficiency and accuracy in terms of both detection speed and effectiveness. The code is available at https://github.com/wuanghoong/Less-is-More.git.","url_abs":"https://arxiv.org/abs/2501.12946v1","url_pdf":"https://arxiv.org/pdf/2501.12946v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"less-is-more-simple-yet-effective-heuristic","repo_url":"https://github.com/wuanghoong/less-is-more","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}