Loading...
Learning Knowledge-diverse Experts for Long-tailed Graph Classification
Mao, Zhengyang Ju, Wei Yi, Siyu Wang, Yifan Xiao, Zhiping Long, Qingqing Yin, Nan Liu, Xinwang Zhang, Ming
Mao, Zhengyang
Ju, Wei
Yi, Siyu
Wang, Yifan
Xiao, Zhiping
Long, Qingqing
Yin, Nan
Liu, Xinwang
Zhang, Ming
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Graph neural networks (GNNs) have shown remarkable success in graph-level classification tasks. However, most of the existing GNN-based studies are based on balanced datasets, while many real-world datasets exhibit long-tailed distributions. In such datasets, the tail classes receive limited attention during training, leading to prediction bias and degraded performance. To address this issue, a range of long-tailed learning strategies have been proposed, such as data re-balancing and transfer learning. However, these approaches encounter several challenges, including insufficient representation capacity for tail classes and their evaluation solely on uniform test data, limiting their capacity to handle unknown class distributions. To tackle these challenges, we introduce a novel framework, namely Knowledge-diverse Experts (KDEX) for long-tailed graph classification. Our KDEX leverages a dynamic memory module to enable the transfer of knowledge from head to tail, which improves the representation ability of the tail. To deal with unknown test distributions, KDEX introduces a knowledge-diverse expert training approach to train experts with different capacities in managing various test distributions. Moreover, we train the hierarchical router in a self-supervised manner to dynamically aggregate each knowledge-diverse expert during testing. Experimental results on multiple benchmarks reveal that our KDEX outperforms current baselines in both standard and test-agnostic long-tailed graph classification.
Citation
Z. Mao et al., “Learning Knowledge-diverse Experts for Long-tailed Graph Classification,” ACM Trans Knowl Discov Data, vol. 19, no. 2, Jan. 2025, doi: 10.1145/3705323/ASSET/AF96566E-7692-4E76-827F-4CB684736C82/ASSETS/GRAPHIC/TKDD-2024-05-0274-F07.JPG.
Source
ACM Transactions on Knowledge Discovery from Data
Conference
Keywords
Graph Neural Network, Long-tailed Learning, Multi-expert Learning
Subjects
Source
Publisher
Association for Computing Machinery