Summary of Training Mlps on Graphs Without Supervision, by Zehong Wang et al.
Training MLPs on Graphs without Supervision
by Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed SimMLP framework leverages self-supervised learning to integrate rich structural information into Multi-Layer Perceptrons (MLPs) for graph learning tasks. By aligning the representations of Graph Neural Networks (GNNs) and neighborhood dependency-free MLPs, SimMLP achieves equivalence to GNNs in optimal cases. Theoretical analysis demonstrates mutual information and inductive bias-based equivalence, highlighting SimMLP’s advanced structural learning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SimMLP is a new way to teach machines to understand graph data without needing lots of training examples. Graphs are like maps that show connections between things. Right now, we use special kinds of artificial intelligence called Graph Neural Networks (GNNs) to learn from these graphs. But GNNs can be slow and not very good at making predictions when they see something new for the first time. SimMLP is different because it uses a type of learning called self-supervised learning to make connections between what’s already in the graph and what might be new. This makes SimMLP really good at predicting things on graphs, especially when we don’t have lots of examples to train with. |
Keywords
» Artificial intelligence » Self supervised
