Summary of Graph Edge Representation Via Tensor Product Graph Convolutional Representation, by Bo Jiang et al.
Graph Edge Representation via Tensor Product Graph Convolutional Representation
by Bo Jiang, Sheng Ge, Ziyan Zhang, Beibei Wang, Jin Tang, Bin Luo
First submitted to arxiv on: 21 Jun 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 proposes a novel convolutional operator for graphs that considers high-dimensional edge features, named Tensor Product Graph Convolution (TPGC). Building upon tensor contraction representation and graph diffusion theories, TPGC aims to obtain effective edge embeddings. This complementary model is designed to address more general graph data analysis with both node and edge features. Experimental results on various graph learning tasks demonstrate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way for computers to understand graphs that have many features attached to their edges, called Tensor Product Graph Convolution (TPGC). This is different from existing methods that mainly focus on nodes in a graph. TPGC helps us analyze these complex graphs better by providing more useful information about the edges. The results of some experiments show that this new approach works well. |
Keywords
» Artificial intelligence » Diffusion
