DeepGCNs: Making GCNs Go as Deep as CNNs (1910.06849v3)

Abstract: Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for their great performance has been the ability to train very deep networks. Despite their huge success in many tasks, CNNs do not work well with non-Euclidean data, which is prevalent in many real-world applications. Graph Convolutional Networks (GCNs) offer an alternative that allows for non-Eucledian data input to a neural network. While GCNs already achieve encouraging results, they are currently limited to architectures with a relatively small number of layers, primarily due to vanishing gradients during training. This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs. We show the benefit of using deep GCNs (with as many as 112 layers) experimentally across various datasets and tasks. Specifically, we achieve very promising performance in part segmentation and semantic segmentation on point clouds and in node classification of protein functions across biological protein-protein interaction (PPI) graphs. We believe that the insights in this work will open avenues for future research on GCNs and their application to further tasks not explored in this paper. The source code for this work is available at https://github.com/lightaime/deep_gcns_torch and https://github.com/lightaime/deep_gcns for PyTorch and TensorFlow implementation respectively.

• The paper introduces novel methods to overcome vanishing gradients by integrating residual, dense, and dilated convolution techniques into GCNs.
• The paper validates DeepGCNs across tasks by training networks up to 112 layers and achieving remarkable metrics, including a 99.43 F1 score on node classification.
• The paper’s approach enables practical advancements in graph analysis for applications such as 3D segmentation, computational biology, and social network analysis.

DeepGCNs: Making GCNs Go as Deep as CNNs

Recent advancements in computer vision have highlighted the prowess of Convolutional Neural Networks (CNNs) in processing Euclidean data. However, extending this performance to non-Euclidean data, which is pervasive in real-world tasks, presents unique challenges. Graph Convolutional Networks (GCNs) offer a promising avenue for addressing these challenges but are historically constrained by limitations in depth, primarily due to issues like vanishing gradients. The paper under discussion, "DeepGCNs: Making GCNs Go as Deep as CNNs", explores innovative methodologies to deepen GCN architectures while retaining stability and performance.

Methodological Innovations

The authors propose the integration of concepts from CNNs, such as residual and dense connections alongside dilated convolutions, into GCNs to mitigate common training obstacles. The incorporation of these techniques enables the design of GCNs that can achieve considerable depths, comparable to those found in CNN architectures.

• Residual and Dense Connections: Leveraging skip connections assists in overcoming the vanishing gradient problem by facilitating better gradient flow across layers. In comparison, dense connections promote feature reuse, enhancing representation power and alleviating computational overheads.
• Dilated Convolutions: These are adapted to dynamically adjust the neighborhood aggregation scale, enhancing the receptive field without proportional increases in computational complexity. This approach allows for richer feature extraction necessary for dense prediction tasks.

Empirical Validation

The paper presents a thorough experimental verification of their DeepGCNs across various domains, including semantic segmentation on point clouds and node classification in biological graphs. Noteworthy results include the successful training and deployment of GCNs with up to 112 layers, achieving superior performance across several benchmarks.

• Semantic and Part Segmentation: When evaluated on datasets such as S3DIS and PartNet, DeepGCNs demonstrated robust improvements in mean intersection over union (mIoU) metrics. This highlights the model's capability in handling complex and irregular data structures typical of real-world 3D sensing tasks.
• Node Classification: On the protein-protein interaction (PPI) dataset, DeepGCNs achieved an impressive F1 score of 99.43, underscoring the method's generalizability and effectiveness across diverse types of graph data.

Implications and Future Directions

The research presents significant implications for both theoretical and practical advancements in GCNs. By unlocking depth-related benefits, these methods pave the way for more sophisticated and capable graph analytical frameworks. Practically, the results hold promise for enhancing tasks like computational biology, recommendation systems, and social network analysis, where non-Euclidean data is prevalent.

Looking forward, future developments could explore additional concepts from the CNN domain and investigate their impacts within GCN frameworks. There's also scope for advancing adaptive mechanisms within GCNs that allow for optimizations based on data-specific characteristics. Moreover, refining these models for efficiency can result in their broader applicability to real-time and large-scale systems, expanding their utility in AI applications.

In conclusion, "DeepGCNs: Making GCNs Go as Deep as CNNs" presents a pivotal step in bridging the performance gap between CNNs and GCNs. It opens a field of opportunities for deeper exploration of non-Euclidean data, facilitating advancements across multiple scientific and industrial domains.