Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space

[2501.15461] Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2501.15461 (cs) [Submitted on 26 Jan 2025 ( v1 ), last revised 10 Apr 2026 (this version, v4)] Title: Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space Authors: Xin He , Yili Wang , Wenqi Fan , Xu Shen , Xin Juan , Rui Miao , Xin Wang View a PDF of the paper titled Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space, by Xin He and 6 other authors View PDF HTML (experimental) Abstract: Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations of GNNs, which struggle to distinguish the importance of information from different neighborhoods. In this paper, we introduce MbaGCN, a novel graph convolutional architecture that draws inspiration from the Mamba paradigm-originally designed for sequence modeling. MbaGCN presents a new backbone for GNNs, consisting of three key components: the Message Aggregation Layer, the Selective State Space Transition Layer, and the Node State Prediction Layer. These components work in tandem to adaptively aggregate neighborhood information, providing greater flexibility and scalability for deep GNN models. While MbaGCN may not consistently outperform all existing methods on each dataset, it provides a foundational framework that demonstrates the effective integration of the Mamba paradigm into graph representation learning. Through extensive experiments on benchmark datasets, we demonstrate that MbaGCN paves the way for future advancements in graph neural network research. Comments: Accepted by The Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2025) Subjects: Machine Learning (cs.LG) Cite as: arXiv:2501.15461 [cs.LG] (or arXiv:2501.15461v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2501.15461 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.24963/ijcai.2025/595 Focus to learn more DOI(s) linking to related resources Submission history From: Xin He [ view email ] [v1] Sun, 26 Jan 2025 09:09:44 UTC (6,482 KB) [v2] Sun, 11 May 2025 11:02:32 UTC (6,485 KB) [v3] Sat, 21 Feb 2026 08:01:53 UTC (6,530 KB) [v4] Fri, 10 Apr 2026 08:17:30 UTC (6,530 KB) Full-text links: Access Paper: View a PDF of the paper titled Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space, by Xin He and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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