Summary of A Scalable and Effective Alternative to Graph Transformers, by Kaan Sancak et al.
A Scalable and Effective Alternative to Graph Transformers
by Kaan Sancak, Zhigang Hua, Jin Fang, Yan Xie, Andrey Malevich, Bo Long, Muhammed Fatih Balin, Ümit V. Çatalyürek
First submitted to arxiv on: 17 Jun 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 Graph Neural Networks (GNNs) have achieved impressive results in graph representation learning, but their limited expressive power hinders capturing long-range dependencies. Graph Transformers (GTs) were introduced to address this, leveraging self-attention mechanisms to model pairwise node relationships. However, GTs suffer from quadratic complexity w.r.t. the number of nodes, limiting their applicability to large graphs. This work presents Graph-Enhanced Contextual Operator (GECO), a scalable alternative that combines neighborhood propagation and global convolutions to capture local and global dependencies in quasilinear time. GECO achieves 169x speedup on synthetic datasets with 2M nodes compared to optimized attention, outperforming traditional GTs on diverse benchmarks, improving the SOTA up to 4.5%. GECO offers a scalable solution for large-scale graph learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding and processing big networks of data. Currently, there are some ways that computers can do this, but they have limitations when dealing with very large networks. The researchers developed a new method called GECO to solve this problem. GECO is faster and more effective than other methods on big networks. It achieves better results than others in many different tests, improving the current best result by up to 4.5%. This means that computers can now process even larger networks more efficiently and accurately. |
Keywords
* Artificial intelligence * Attention * Representation learning * Self attention
