Just Accepted
Learning Knowledge-diverse Experts for Long-tailed Graph Classification
Authors: 
Zhengyang Mao, Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Qingqing Long, Nan Yin, Xinwang Liu, Ming ZhangAuthors Info & Claims
Accepted on 10 November 2024
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.
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Publication History
Online AM: 22 November 2024
Accepted: 10 November 2024
Revised: 19 August 2024
Received: 24 May 2024
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