{"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/distance-recomputator-and-topology","title":"Distance Recomputator and Topology Reconstructor for Graph Neural Networks","arxiv_id":"2406.17281","date":"2024-06-25","proceeding":null,"authors":["Dong Liu","Meng Jiang"],"abstract":"This paper introduces novel methodologies, the Distance Recomputator and Topology Reconstructor, aimed at enhancing Graph Neural Networks (GNNs). The Distance Recomputator dynamically recalibrates node distances within k-hop neighborhoods using a dynamic encoding scheme, thereby improving the accuracy and adaptability of node representations. Concurrently, the Topology Reconstructor adjusts local graph structures based on computed \"similarity distances,\" optimizing network configurations for improved learning outcomes. These methods address the limitations of static node representations and fixed aggregation schemes in traditional GNNs, offering a more nuanced approach to modeling complex and dynamic graph topologies. Furthermore, our experimental evaluations demonstrate significant performance advantages over existing methods across various benchmark datasets. The proposed Distance Recomputator and Topology Reconstructor not only enhance node relationship modeling accuracy but also optimize information aggregation efficiency through an asynchronous aggregation mechanism. This approach proves particularly effective in scenarios involving dynamic or large-scale graphs, showcasing the methods' robustness and applicability in real-world graph learning tasks.","url_abs":"https://arxiv.org/abs/2406.17281v1","url_pdf":"https://arxiv.org/pdf/2406.17281v1.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":"abstracts"},"code_links":[{"paper_slug":"distance-recomputator-and-topology","repo_url":"https://github.com/NoakLiu/Distance-Recomputator-and-Topology-Reconstructor","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"graph-learning","task_name":"Graph Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2406.17281","atlas_url":"https://app.syntology.ai/?focus=2406.17281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17281"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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