Summary of Stg-mamba: Spatial-temporal Graph Learning Via Selective State Space Model, by Lincan Li et al.
STG-Mamba: Spatial-Temporal Graph Learning via Selective State Space Model
by Lincan Li, Hanchen Wang, Wenjie Zhang, Adelle Coster
First submitted to arxiv on: 19 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Spatial-Temporal Graph Mamba (STG-Mamba), a novel approach to learning spatial-temporal graph (STG) data by leveraging selective state space models. Unlike previous GNN-based methods that focus solely on node relationships, STG-Mamba treats the STG network as a system and explores its dynamic state evolution across time. The proposed Spatial-Temporal Selective State Space Module (ST-S3M) precisely focuses on selected STG latent features. Additionally, Kalman Filtering Graph Neural Networks (KFGN) are introduced to dynamically integrate and upgrade STG embeddings from different temporal granularities through a learnable Kalman Filtering statistical theory-based approach. The paper conducts extensive empirical studies on three benchmark STG forecasting datasets, demonstrating the performance superiority and computational efficiency of STG-Mamba. It surpasses existing state-of-the-art methods in terms of STG forecasting performance while reducing computational cost and test inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn complex data called Spatial-Temporal Graphs (STGs). These graphs change over time and are made up of different types of information. The current methods for learning these graphs only look at how the nodes in the graph are related to each other, but they don’t consider the underlying features that exist in the system over time. This paper proposes a new approach called STG-Mamba, which treats the graph as a whole and explores its changing state over time. It also introduces a new module called ST-S3M that helps identify important features in the graph. The results show that this new approach is better than existing methods at forecasting what will happen next in the graph, while also being more efficient. |
Keywords
* Artificial intelligence * Gnn * Inference
