Summary of Tangnn: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism For Graph Representation Learning, by Jiawei E et al.
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation Learning
by Jiawei E, Yinglong Zhang, Xuewen Xia, Xing Xu
First submitted to arxiv on: 23 Nov 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 an innovative Graph Neural Network (GNN) architecture that integrates Top-m attention mechanism aggregation and neighborhood aggregation components to enhance the model’s ability to capture distant vertex relationships. The new approach improves computational efficiency while enriching node features, facilitating deeper analysis of complex graph structures. The proposed method is applied to citation sentiment prediction, a novel task in the GNN field, using ArXivNet, a dedicated citation network with annotated sentiment polarity. Experimental results demonstrate superior performance across tasks including vertex classification, link prediction, sentiment prediction, graph regression, and visualization, outperforming existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about improving computer models that analyze complicated data structures called graphs. Graphs are like networks of connected dots or nodes. The problem with current models is that they can’t handle distant relationships between these nodes very well. To solve this issue, the researchers created a new model that combines two techniques to better understand graph patterns. They tested this new model on a dataset of scientific citations and found it worked much better than previous methods. This breakthrough could lead to more accurate predictions and better understanding of complex systems. |
Keywords
» Artificial intelligence » Attention » Classification » Gnn » Graph neural network » Regression
