Venue
- ICASSP 2022
Date
- 2022
Heterogeneous Graph Node Classification with Multi-Hops Relation Features
Xiaolong Xu*
Hong Jin*
Weiqiang Wang*
Shuo Jia*
* External authors
ICASSP 2022
2022
Abstract
In recent years, knowledge graph~(KG) has obtained many achievements in both research and industrial fields. However, most KG algorithms consider node embedding with only structure and node features, but not relation features. In this paper, we propose a novel Heterogeneous Attention~(HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges on the KG. To better capture representations, multi-hop relation features are involved to generate edge embeddings and help the model obtain more semantic information. To capture a more complex representation, we design different encoder parameters for different types of nodes and edges in HAT. Extensive experiments validate that our HAT significantly outperforms the state-of-the-art methods on both the public datasets and a large-scale real-world fintech dataset.
Related Publications
Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…
Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this prob…
In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptat…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.