Summary of Hetegraph-mamba: Heterogeneous Graph Learning Via Selective State Space Model, by Zhenyu Pan et al.
HeteGraph-Mamba: Heterogeneous Graph Learning via Selective State Space Model
by Zhenyu Pan, Yoonsung Jeong, Xiaoda Liu, Han Liu
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 proposed heterogeneous graph mamba network (HGMN) utilizes selective state space models (SSSMs) for learning on heterogeneous graphs, addressing two major challenges: capturing long-range dependencies among nodes and adapting SSSMs to heterogeneous data. The key contribution is a general graph architecture that can solve real-world scenarios efficiently. A two-level tokenization approach is introduced, first capturing dependencies within identical node types and then across all types. Experimental results demonstrate the framework’s superiority in accuracy and efficiency over 19 state-of-the-art methods on heterogeneous benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to learn from complex graphs with different types of nodes. It’s called HGMN, which uses special models (SSSMs) that can handle this kind of data. The approach helps solve two big problems: finding relationships between nodes and adapting the model to fit the graph. The authors introduce a new way to break down the graph into smaller parts to make it easier to learn from. They test their method on many existing approaches and show that it’s better in terms of getting accurate answers and being efficient. |
Keywords
» Artificial intelligence » Tokenization
