Summary of Graphmore: Mitigating Topological Heterogeneity Via Mixture Of Riemannian Experts, by Zihao Guo et al.
GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
by Zihao Guo, Qingyun Sun, Haonan Yuan, Xingcheng Fu, Min Zhou, Yisen Gao, Jianxin Li
First submitted to arxiv on: 15 Dec 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 This paper proposes the Graph Mixture of Riemannian Experts (GraphMoRE) framework to address the challenge of topological heterogeneity in real-world graphs. The existing methods for learning graph representations are limited by their reliance on single constant curvature spaces, which fail to capture the complex geometric shapes and result in low-quality embeddings with high distortion. Graph foundation models require a uniform handling of diverse graph data, but current approaches are insufficient. Product manifolds have shown promise in addressing topological heterogeneity, but they remain homogeneous and inflexible. The proposed framework uses personalized fine-grained topology geometry pattern preservation to effectively tackle topological heterogeneity. A topology-aware gating mechanism selects the optimal embedding space for each node, minimizing distortion. By fusing diverse Riemannian experts with learned gating weights, a heterogeneous manifold is constructed with varying curvatures at different points. An alignment strategy measures pairwise distances between different embedding spaces fairly. Experimental results on real-world and synthetic datasets demonstrate superior performance with lower distortion, highlighting the potential for modeling complex graphs with topological heterogeneity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph MoRe is a new way to understand and work with complex graphs that have many different patterns. Right now, computers are not good at understanding these kinds of graphs because they use the same method for all graphs. But real-world graphs are like people – each one is unique and has its own special characteristics. The GraphMoRE system tries to fix this by creating a special space just for each graph, so it can understand and work with them better. |
Keywords
» Artificial intelligence » Alignment » Embedding space
