Summary of Simmlp: Training Mlps on Graphs Without Supervision, by Zehong Wang et al.
SimMLP: Training MLPs on Graphs without Supervision
by Zehong Wang, Zheyuan Zhang, Chuxu Zhang, Yanfang Ye
First submitted to arxiv on: 14 Feb 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 from graph context-aware GNNs into neighborhood dependency-free MLPs, achieving equivalence to GNNs in the optimal case. This approach enables the acceleration of inference in latency-sensitive applications while maintaining the benefits of incorporating structural insights. The framework is theoretically analyzed using mutual information and inductive bias, highlighting its advanced structural learning capabilities. Extensive experiments on 20 benchmark datasets demonstrate SimMLP’s superiority over state-of-the-art baselines, particularly in scenarios involving unseen nodes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SimMLP is a new way to use artificial intelligence to learn from graph data. Currently, these types of models are not very good at handling complex relationships between things in the data. To fix this, researchers came up with an idea to take information from one type of model and put it into another type of model that can work faster. This allows for more efficient processing of large amounts of data. The new approach was tested on many different types of graph problems and showed significant improvements over existing methods. |
Keywords
* Artificial intelligence * Inference * Self supervised
