Towards Deeper Graph Neural Networks (2007.09296v1)

Abstract: Graph neural networks have shown significant success in the field of graph representation learning. Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Nevertheless, one layer of these neighborhood aggregation methods only consider immediate neighbors, and the performance decreases when going deeper to enable larger receptive fields. Several recent studies attribute this performance deterioration to the over-smoothing issue, which states that repeated propagation makes node representations of different classes indistinguishable. In this work, we study this observation systematically and develop new insights towards deeper graph neural networks. First, we provide a systematical analysis on this issue and argue that the key factor compromising the performance significantly is the entanglement of representation transformation and propagation in current graph convolution operations. After decoupling these two operations, deeper graph neural networks can be used to learn graph node representations from larger receptive fields. We further provide a theoretical analysis of the above observation when building very deep models, which can serve as a rigorous and gentle description of the over-smoothing issue. Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields. A set of experiments on citation, co-authorship, and co-purchase datasets have confirmed our analysis and insights and demonstrated the superiority of our proposed methods.

Towards Deeper Graph Neural Networks

The paper "Towards Deeper Graph Neural Networks" presents a compelling paper on enhancing the depth and capabilities of Graph Neural Networks (GNNs) while addressing the prevalent over-smoothing issue that hampers performance as these networks grow deeper. The authors identify a key challenge in current GNN architectures: the entanglement of representation transformation and propagation. By decoupling these processes, they propose methods to construct deeper networks capable of leveraging larger receptive fields without significant performance degradation.

Insights and Contributions

The authors first analyze the deterioration in performance observed with deeper architectures. They propose a metric for measuring node representation smoothness, observing that the primary factor limiting performance is not over-smoothing in moderate depths but rather the intertwinement of transformation and propagation in the GNN layers. This analysis challenges a commonly held belief in the community and provides a new perspective on the design of GNN architectures.

A significant theoretical contribution of the paper is the detailed examination of the propagation operation, particularly how representation transformation and propagation can be decoupled. The authors prove that propagating information infinitely in very deep models leads to indistinguishable node representations, thus rigorously characterizing the over-smoothing phenomenon.

Deep Adaptive Graph Neural Network (DAGNN)

Building on these insights, the authors propose the Deep Adaptive Graph Neural Network (DAGNN), a novel architecture that decouples transformation from propagation to allow for deeper models. DAGNN incorporates an adaptive mechanism to effectively manage information from various receptive fields, enabling it to balance local and global information for each node dynamically. This adaptive feature contributes significantly to DAGNN's robustness and superior performance across various datasets.

Experimental Results

The empirical evaluation on citation, co-authorship, and co-purchase datasets demonstrates DAGNN's effectiveness over other state-of-the-art baselines. The results reflect not only improvements in accuracy but also stability across models when permitting larger receptive fields. Notably, DAGNN's ability to perform well under limited training data conditions showcases its practical utility, leveraging global context effectively.

Implications and Future Directions

The decoupling of transformation and propagation operations and the adaptive adjustment mechanism in DAGNN highlight an important shift in GNN design, moving towards architectures that are scalable and robust to changes in the depth of the network. Further exploration into adaptive mechanisms could extend the capabilities of GNNs in various settings, including those with dynamic graph structures or real-time updates.

The theoretical findings prompt more systematic approaches to tackle the challenges associated with deep networks, providing a foundation for further theoretical studies. This could foster developments in designing new graph operations tailored for specific applications, potentially leading to advancements in areas demanding high-level abstraction and information integration, such as social network analysis and recommendation systems.

In conclusion, the research presented in this paper offers substantive strides towards constructing deeper, more capable graph neural networks. By addressing critical issues associated with depth and proposing innovative solutions, it opens new avenues for future research and development in the field of graph representation learning.