Summary of Graph Mamba: Towards Learning on Graphs with State Space Models, by Ali Behrouz and Farnoosh Hashemi
Graph Mamba: Towards Learning on Graphs with State Space Models
by Ali Behrouz, Farnoosh Hashemi
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a novel approach to Graph Neural Networks (GNNs) called Graph Mamba Networks (GMNs), which combines the strengths of State Space Models (SSMs) with graph-structured data. GMNs address two major limitations of traditional GNNs: over-squashing and poor capturing of long-range dependencies. The framework consists of four required steps: Neighborhood Tokenization, Token Ordering, Architecture of Bidirectional Selective SSM Encoder, and Local Encoding, as well as an optional step for Positional/Structural Encodings (SE/PE). Experimental results demonstrate the outstanding performance of GMNs on various benchmark datasets, including long-range, small-scale, large-scale, and heterophilic datasets. This work shows that complex message-passing and SE/PE are not necessary for good performance, offering a more efficient and effective alternative to traditional GNNs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GMNs are a new type of Graph Neural Networks that use State Space Models (SSMs) to learn graph representations. This approach is different from traditional GNNs that use message-passing mechanisms. The authors show that GMNs can perform well on various benchmark datasets, including long-range and small-scale datasets. They also explain why their method works better than others. |
Keywords
* Artificial intelligence * Encoder * Token * Tokenization
