Enhancing Graph Neural Networks: A Mutual Learning Approach
Paul Agbaje, Arkajyoti Mitra, Afia Anjum, Pranali Khose, Ebelechukwu Nwafor, Habeeb Olufowobi
TL;DR
This paper introduces a collaborative learning framework for GNNs where multiple models mutually teach each other, enhancing performance without pre-trained teachers, through adaptive weighting and entropy techniques, validated on multiple datasets.
Contribution
It proposes a novel mutual learning approach for GNNs with adaptive logit weighting and entropy enhancement, improving multi-task performance without pre-trained teachers.
Findings
Mutual learning improves GNN performance across tasks.
Adaptive weighting and entropy techniques enhance knowledge exchange.
Effective on multiple node and graph classification datasets.
Abstract
Knowledge distillation (KD) techniques have emerged as a powerful tool for transferring expertise from complex teacher models to lightweight student models, particularly beneficial for deploying high-performance models in resource-constrained devices. This approach has been successfully applied to graph neural networks (GNNs), harnessing their expressive capabilities to generate node embeddings that capture structural and feature-related information. In this study, we depart from the conventional KD approach by exploring the potential of collaborative learning among GNNs. In the absence of a pre-trained teacher model, we show that relatively simple and shallow GNN architectures can synergetically learn efficient models capable of performing better during inference, particularly in tackling multiple tasks. We propose a collaborative learning framework where ensembles of student GNNs…
Peer Reviews
Decision·Submitted to ICLR 2025
The paper addresses the significance of enhancing GNNs through collaborative learning, which is essential for tasks requiring high generalization in node and graph classification. The presentation is easy to follow.
1. The innovative aspects of the proposed methods are limited. The distinction between GML and similar approaches, such as those in [2], needs to be more clearly articulated to highlight the unique contributions. 2. Important baselines, specifically state-of-the-art knowledge distillation methods ([1,2,3]), are missing from Table 1. Including these would provide a more comprehensive evaluation. 3. The paper should clarify if the performance improvements in Tables 2 and 3 result from the enhanc
1 - The concept of mutual learning among GNNs without a teacher model is innovative and potentially impactful for improving shallow GNN models. 2 - The adaptive logit weighting and entropy-based uncertainty enhancement components are well-motivated for improving model generalization and adaptability. 3 - The paper provides extensive experimental results on multiple node and graph classification datasets, demonstrating the method's effectiveness in various scenarios.
1 - The paper lacks a comparison with state-of-the-art GNN models and other existing knowledge distillation techniques, which limits the understanding of how well the proposed approach performs relative to current advancements in GNNs. 2 - The experiments primarily focus on shallow GNN models without including stronger GNN architectures as baselines. Including state-of-the-art GNNs would provide a more robust evaluation. 3 - The quality of the figures is subpar, making them hard to read and in
- The mutual learning approach provides a new angle on GNNs knowledge sharing, expanding the conventional KD paradigm. This collective approach allows models to generalize more effectively without a pre-trained teacher. - Experimental results on both node and graph classification tasks demonstrate the capacity of GML to improve performance. The MLP adaptation for KD highlights the versatility of the approach and the benefits of faster inference. - The paper is clearly written and easy to follo
- The experiments rely on standard datasets with relatively small sizes (e.g., Cora, Citeseer, OGB-bace and bbbp, etc), which may not fully capture the potential of GML on larger, real-world graph datasets. Including experiments on large-scale or complex datasets would strengthen the paper’s impact. For example, OGB provides much larger datasets for both node and graph classification. - The extension from traditional teacher-student distillation to two peers collaborative learning is interestin
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Taxonomy
TopicsAdvanced Graph Neural Networks · Topic Modeling · Graph Theory and Algorithms