Summary of Learning to Control the Smoothness Of Graph Convolutional Network Features, by Shih-hsin Wang et al.
Learning to Control the Smoothness of Graph Convolutional Network Features
by Shih-Hsin Wang, Justin Baker, Cory Hauck, Bao Wang
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirical correlation between node classification accuracy and the ratio of smooth to non-smooth feature components. However, the optimal ratio that favors node classification is unknown, and the non-smooth features of deep GCN with ReLU or leaky ReLU activation function diminish. In this paper, we propose a new strategy to let GCN learn node features with a desired smoothness – adapting to data and tasks – to enhance node classification. Our approach has three key steps: (1) We establish a geometric relationship between the input and output of ReLU or leaky ReLU. (2) Building on our geometric insights, we augment the message-passing process of graph convolutional layers (GCLs) with a learnable term to modulate the smoothness of node features with computational efficiency. (3) We investigate the achievable ratio between smooth and non-smooth feature components for GCNs with the augmented message-passing scheme. Our extensive numerical results show that the augmented message-passing schemes significantly improve node classification for GCN and some related models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make graph convolutional networks (GCNs) better at learning node features. Researchers have found a connection between how smooth or non-smooth these features are and how well the network can classify nodes. However, they haven’t figured out the perfect ratio of smooth to non-smooth features for this task. The authors propose a new approach that adapts to different data and tasks to make GCNs learn node features with the right amount of smoothness. They achieve this by creating a relationship between the input and output of certain activation functions, then adjusting the way messages are passed through the network to control the smoothness. Their results show that this method improves node classification for GCNs. |
Keywords
» Artificial intelligence » Classification » Convolutional network » Gcn » Relu
