Graph Representation Learning via Aggregation Enhancement
This work addresses a key bottleneck in GNNs for graph-structured data, offering an incremental improvement with practical benefits for deep networks.
The paper tackles the challenge of effective information aggregation in graph neural networks (GNNs) by introducing a kernel regression (KR) loss, which improves performance in node classification tasks, achieving substantial gains over state-of-the-art methods on multiple datasets.
Graph neural networks (GNNs) have become a powerful tool for processing graph-structured data but still face challenges in effectively aggregating and propagating information between layers, which limits their performance. We tackle this problem with the kernel regression (KR) approach, using KR loss as the primary loss in self-supervised settings or as a regularization term in supervised settings. We show substantial performance improvements compared to state-of-the-art in both scenarios on multiple transductive and inductive node classification datasets, especially for deep networks. As opposed to mutual information (MI), KR loss is convex and easy to estimate in high-dimensional cases, even though it indirectly maximizes the MI between its inputs. Our work highlights the potential of KR to advance the field of graph representation learning and enhance the performance of GNNs. The code to reproduce our experiments is available at https://github.com/Anonymous1252022/KR_for_GNNs