Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks
This addresses the problem of training deeper GCNs for researchers and practitioners in graph machine learning, offering an incremental improvement by focusing on training procedures rather than architectural changes.
The paper tackles the performance limit of Graph Convolutional Networks (GCNs) by identifying training difficulty due to graph signal energy loss in backward passes, and proposes operator modifications that significantly reduce training difficulties and boost performance without changing parameters.
The performance limit of Graph Convolutional Networks (GCNs) and the fact that we cannot stack more of them to increase the performance, which we usually do for other deep learning paradigms, are pervasively thought to be caused by the limitations of the GCN layers, including insufficient expressive power, etc. However, if so, for a fixed architecture, it would be unlikely to lower the training difficulty and to improve performance by changing only the training procedure, which we show in this paper not only possible but possible in several ways. This paper first identify the training difficulty of GCNs from the perspective of graph signal energy loss. More specifically, we find that the loss of energy in the backward pass during training nullifies the learning of the layers closer to the input. Then, we propose several methodologies to mitigate the training problem by slightly modifying the GCN operator, from the energy perspective. After empirical validation, we confirm that these changes of operator lead to significant decrease in the training difficulties and notable performance boost, without changing the composition of parameters. With these, we conclude that the root cause of the problem is more likely the training difficulty than the others.