MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
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Graph-mamba: Towards long-range graph sequence modeling with se- lective state spaces.arXiv preprint arXiv:2402.00789
13 Pith papers cite this work, alongside 20 external citations. Polarity classification is still indexing.
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abstract
Attention mechanisms have been widely used to capture long-range dependencies among nodes in Graph Transformers. Bottlenecked by the quadratic computational cost, attention mechanisms fail to scale in large graphs. Recent improvements in computational efficiency are mainly achieved by attention sparsification with random or heuristic-based graph subsampling, which falls short in data-dependent context reasoning. State space models (SSMs), such as Mamba, have gained prominence for their effectiveness and efficiency in modeling long-range dependencies in sequential data. However, adapting SSMs to non-sequential graph data presents a notable challenge. In this work, we introduce Graph-Mamba, the first attempt to enhance long-range context modeling in graph networks by integrating a Mamba block with the input-dependent node selection mechanism. Specifically, we formulate graph-centric node prioritization and permutation strategies to enhance context-aware reasoning, leading to a substantial improvement in predictive performance. Extensive experiments on ten benchmark datasets demonstrate that Graph-Mamba outperforms state-of-the-art methods in long-range graph prediction tasks, with a fraction of the computational cost in both FLOPs and GPU memory consumption. The code and models are publicly available at https://github.com/bowang-lab/Graph-Mamba.
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A hyperbolic-space framework with geometric entailment constraints and a graph-aware Mamba module improves brain-network classification of ASD and MDD by explicitly modeling ROI-to-community-to-whole-brain hierarchy.
SFMambaNet combines a Local Spectral-Geometric Attention block with a Spectral-Integrated Global Mamba block to improve inlier-outlier separation in two-view correspondence pruning.
ExPath is a subgraph inference framework that classifies bio-networks with experimental data and uses explanations to identify targeted pathways, reporting up to 4.5x higher Fidelity+ and 14x lower Fidelity- than baselines on 301 networks.
Gated linear attention Transformers achieve competitive language modeling results with linear-time inference, superior length generalization, and higher training throughput than Mamba.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
3DMambaComplete applies the Mamba model to point cloud completion via hyperpoint generation, spatial spreading, and mesh deformation, claiming better results than prior methods on benchmarks.
EnergyMamba improves energy consumption prediction accuracy by about 5% and uncertainty quantification by about 6% over 15 baselines on four real-world US datasets by combining graph-enhanced Mamba with adaptive sequential conformalized quantile regression.
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
citing papers explorer
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Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
MbaGCN combines message aggregation, selective state space transitions, and node state prediction to create a more scalable deep graph convolutional network.
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Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
A hyperbolic-space framework with geometric entailment constraints and a graph-aware Mamba module improves brain-network classification of ASD and MDD by explicitly modeling ROI-to-community-to-whole-brain hierarchy.
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SFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning
SFMambaNet combines a Local Spectral-Geometric Attention block with a Spectral-Integrated Global Mamba block to improve inlier-outlier separation in two-view correspondence pruning.
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ExPath: Targeted Pathway Inference for Biological Knowledge Bases via Graph Learning and Explanation
ExPath is a subgraph inference framework that classifies bio-networks with experimental data and uses explanations to identify targeted pathways, reporting up to 4.5x higher Fidelity+ and 14x lower Fidelity- than baselines on 301 networks.
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Gated Linear Attention Transformers with Hardware-Efficient Training
Gated linear attention Transformers achieve competitive language modeling results with linear-time inference, superior length generalization, and higher training throughput than Mamba.
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
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Graph Mamba Survival Analysis Based on Topology-Aware ordering
TopoMamSurv introduces topology-aware ordering and bidirectional Mamba with GCN for efficient WSI graph survival analysis, claiming performance gains on five TCGA datasets.
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S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
S³GNN mitigates oversquashing in message-passing networks via lightweight global mixing without strong prior assumptions, yielding up to 10x error reduction and 50% fewer parameters across multiple domains.
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3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion
3DMambaComplete applies the Mamba model to point cloud completion via hyperpoint generation, spatial spreading, and mesh deformation, claiming better results than prior methods on benchmarks.
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EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
EnergyMamba improves energy consumption prediction accuracy by about 5% and uncertainty quantification by about 6% over 15 baselines on four real-world US datasets by combining graph-enhanced Mamba with adaptive sequential conformalized quantile regression.
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State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
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A Survey of Mamba
The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.
- Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba