Summary of Hyperbolic Heterogeneous Graph Attention Networks, by Jongmin Park et al.
Hyperbolic Heterogeneous Graph Attention Networks
by Jongmin Park, Seunghoon Han, Soohwan Jeong, Sungsu Lim
First submitted to arxiv on: 15 Apr 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 This paper presents Hyperbolic Heterogeneous Graph Attention Networks (HHGAT), a novel approach to learn vector representations of elements in complex heterogeneous graphs. Unlike previous methods, which embed nodes in Euclidean space, HHGAT leverages hyperbolic spaces and meta-path instances to better capture hierarchical or power-law structures inherent in these graphs. The authors conduct experiments on three real-world datasets, showcasing that HHGAT outperforms state-of-the-art models in node classification and clustering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to draw a map of the internet, where different types of connections between websites form a complex network. Most methods try to shrink this map into a flat, two-dimensional space, which can distort its true shape. This paper proposes a new way to represent these networks, using special mathematical spaces called hyperbolic spaces that better fit their complex structures. The authors test their method on real-world internet data and show it does a better job of recognizing patterns and grouping similar websites together. |
Keywords
» Artificial intelligence » Attention » Classification » Clustering
