Summary of Hi-gmae: Hierarchical Graph Masked Autoencoders, by Chuang Liu et al.
Hi-GMAE: Hierarchical Graph Masked Autoencoders
by Chuang Liu, Zelin Yao, Yibing Zhan, Xueqi Ma, Dapeng Tao, Jia Wu, Wenbin Hu, Shirui Pan, Bo Du
First submitted to arxiv on: 17 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 proposed Hierarchical Graph Masked AutoEncoders (Hi-GMAE) framework addresses the limitations of existing single-scale GMAEs in capturing hierarchical structures within graphs. By constructing a multi-scale graph hierarchy through graph pooling, Hi-GMAE enables the exploration of graph structures across different granularity levels. A novel coarse-to-fine strategy ensures masking uniformity of subgraphs across these scales, while a gradual recovery strategy mitigates learning challenges posed by completely masked subgraphs. Unlike traditional GNNs, Hi-GMAE’s encoder and decoder are modified into hierarchical structures, utilizing GNN for local analysis at finer scales and graph transformer for global information capture at coarser scales. Experimental results on 15 graph datasets demonstrate Hi-GMAE’s superiority over 17 state-of-the-art self-supervised competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hi-GMAE is a new way to learn about graph structures, which are like hierarchical trees. Existing methods only look at one level of detail, but real-world graphs have many levels. This makes it hard for those methods to capture important information. Hi-GMAE fixes this by looking at multiple levels of detail and using special strategies to make sure the learning process is stable. It’s different from other graph neural networks because it uses a combination of local analysis (like zooming in) and global understanding (like seeing the big picture). This approach works better than many other methods on 15 different datasets. |
Keywords
» Artificial intelligence » Decoder » Encoder » Gnn » Self supervised » Transformer
