Summary of Hhgt: Hierarchical Heterogeneous Graph Transformer For Heterogeneous Graph Representation Learning, by Qiuyu Zhu et al.
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
by Qiuyu Zhu, Liang Zhang, Qianxiong Xu, Kaijun Liu, Cheng Long, Xiaoyang Wang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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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 Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), existing methods have limitations. Research has turned to Graph Transformers (GTs) for enhanced HIN representation learning, but this field is still underdeveloped. Two key shortcomings in current work are the uniform treatment of neighbors and the mixing of nodes with different types during aggregation. To address these gaps, we propose an innovative structure called (k,t)-ring neighborhood that organizes nodes by distance and type. This structure enables the development of a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model that integrates Type-level and Ring-level Transformers for aggregating nodes of diverse types and distances. Our approach is evaluated on downstream tasks, achieving notable improvements in node clustering tasks, with up to 24.75% and 29.25% increases in NMI and ARI, respectively, compared to the best baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been working on creating better models for understanding complex networks like social media or scientific collaborations. Current methods are limited because they don’t take into account the different types of connections between people or things. To solve this problem, we created a new way of organizing these connections based on distance and type. This allowed us to develop a new model that can learn more effectively from these complex networks. Our model was tested on some common tasks and performed much better than previous methods. |
Keywords
* Artificial intelligence * Clustering * Representation learning * Transformer
