Summary of Teaching Mlps to Master Heterogeneous Graph-structured Knowledge For Efficient and Accurate Inference, by Yunhui Liu et al.
Teaching MLPs to Master Heterogeneous Graph-Structured Knowledge for Efficient and Accurate Inference
by Yunhui Liu, Xinyi Gao, Tieke He, Jianhua Zhao, Hongzhi Yin
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Heterogeneous Graph Neural Networks (HGNNs) have shown promising results in various heterogeneous graph learning tasks by capturing intricate relationships and diverse relational semantics. However, the neighborhood-fetching latency incurred by structure dependency makes it challenging to deploy HGNNs for latency-constrained applications. To address this issue, researchers introduce HG2M and HG2M+ which combine HGNN’s performance with MLP’s efficient inference. These models directly train student MLPs with node features as input and soft labels from teacher HGNNs as targets, or further distill reliable semantic knowledge into student MLPs through node and meta-path distillation. Experimental results on six heterogeneous graph datasets show that HG2Ms can achieve competitive or even better performance than HGNNs while significantly outperforming vanilla MLPs. Moreover, HG2Ms demonstrate a 379.24speedup in inference over HGNNs on the large-scale IGB-3M-19 dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Heterogeneous Graph Neural Networks (HGNNs) are special kinds of artificial intelligence that work well with complex data structures called heterogeneous graphs. These graphs can represent many things, like social networks or chemical compounds. The problem is that HGNNs take a long time to make predictions because they need to look at all the relationships between different parts of the graph. To solve this problem, researchers created two new models called HG2M and HG2M+. They work by training smaller models (MLPs) to imitate the behavior of the HGNNs. This makes them faster and more efficient. The new models were tested on six different datasets and performed just as well or even better than the original HGNNs. Additionally, they can make predictions much faster. |
Keywords
» Artificial intelligence » Distillation » Inference » Semantics
