Summary of Graph Similarity Regularized Softmax For Semi-supervised Node Classification, by Yiming Yang et al.
Graph Similarity Regularized Softmax for Semi-Supervised Node Classification
by Yiming Yang, Jun Liu, Wei Wan
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 a novel approach to semi-supervised node classification using Graph Neural Networks (GNNs). The authors identify a limitation in traditional softmax functions, which lack spatial information from the graph structure. To address this, they introduce a graph similarity regularized softmax function that incorporates non-local total variation regularization into the activation function. This allows the model to better capture inherent graph structures. The proposed method is applied to GCN and GraphSAGE architectures on citation and webpage linking datasets, respectively. Experimental results demonstrate improved node classification accuracy and generalization capabilities, particularly on disassortative graphs. The authors’ approach shows promise for semi-supervised learning in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve a type of AI model called Graph Neural Networks (GNNs). GNNs are good at processing data that has connections or relationships between things. The problem is that these models can’t fully understand the underlying structure of this data. To fix this, the researchers developed a new way to make GNNs work better by adding some extra information about the graph’s shape and layout. They tested their approach on two different types of data: citations and webpage links. The results show that their method is effective in identifying nodes (things) based on limited training data. This could have important implications for many real-world applications. |
Keywords
» Artificial intelligence » Classification » Gcn » Generalization » Regularization » Semi supervised » Softmax
