Partially Trained Graph Convolutional Networks Resist Oversmoothing
This work addresses oversmoothing in GCNs for graph learning tasks, offering an incremental improvement by leveraging untrained layers to enhance node embeddings in data-scarce settings.
The paper tackles the problem of oversmoothing in Graph Convolutional Networks (GCNs) by investigating partially trained GCNs, where only a single layer is trained while others remain frozen, and shows that this approach reduces oversmoothing and improves performance in cold-start scenarios with limited node feature information.
In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the effect of training only a single layer of a GCN, while keeping the rest of the layers frozen. We propose a basis on which the effect of the untrained layers and their contribution to the generation of embeddings can be predicted. Moreover, we show that network width influences the dissimilarity of node embeddings produced after the initial node features pass through the untrained part of the model. Additionally, we establish a connection between partially trained GCNs and oversmoothing, showing that they are capable of reducing it. We verify our theoretical results experimentally and show the benefits of using deep networks that resist oversmoothing, in a ``cold start'' scenario, where there is a lack of feature information for unlabeled nodes.