Summary of Teaching Mlp More Graph Information: a Three-stage Multitask Knowledge Distillation Framework, by Junxian Li et al.
Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework
by Junxian Li, Bin Shi, Erfei Cui, Hua Wei, Qinghua Zheng
First submitted to arxiv on: 2 Mar 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 addresses the issue of Graph Neural Networks (GNNs) consuming excessive time and memory for inference tasks on large-scale graph datasets. To overcome this, the authors propose a novel three-stage multitask distillation framework that reduces reliance on graph structure. The framework consists of Positional Encoding to capture positional information and Neural Heat Kernels for graph data processing in GNNs. Additionally, hidden layer outputs are matched for better performance of student MLP’s hidden layers. This work is the first to include hidden layer distillation for student MLPs on graphs and combines graph Positional Encoding with MLP. The authors test their approach’s performance and robustness across various settings, concluding that it outperforms well while maintaining good stability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a way to make computers learn from very large sets of connected data (called graphs). Right now, these systems are really slow and use a lot of memory. The authors want to fix this by making the computer learn in a different way. They come up with a new method that uses special codes to capture important information and makes the computer process the graph data better. This is the first time someone has done something like this, and it seems to work really well. |
Keywords
* Artificial intelligence * Distillation * Inference * Positional encoding
