Project DetailsGraph Neural Networks (GNNs) have become powerful tools for processing graph-structed data, with graph convolution as a fundamental building block. Typically, graph convolution is approximated by message passing between immediate neighbors, which can limit the model’s ability to capture more complex relationships. To address this limitation, Gasteiger et al. introduce Graph Diffusion Convolution (GDC) in their paper “Diffusion Improves Graph Learning,” introducing an innovative approach that extends the scope of convolution beyond immediate neighbors, while preserving spatial awareness. GDC leverages generalized graph diffusion techniques, including heat kernel and personalized PageRank (PPR), to effectively mitigate the impact of noisy or arbitrarily defined edges often encountered in real-world graphs.
GDC bridges the gap between spatial and spectral methods, effectively integrating their strengths. By replacing message passing with graph diffusion convolution, we achieve consistent and significant performance improvement across various models and tasks, including both supervised and unsupervised learning on diverse datasets. GDC’s versatility is further highlighted by its ability to seamlessly integrate with existing graph-based models or algorithms, e.g. spectral clustering, without requiring additional modifications or impacting computational complexity.
Building on this research, we explored GDC’s applicability in different domains by applying it to a hyperspectral imaging dataset, a relatively unexplored area within graph-based learning. Our analysis revealed that GDC yields noticeable improvements over simple solutions, highlighting its potential for expanding graph-based learning techniques to this relatively uncharted domain. We then identified promising techniques for further improving its performance, which could lead to improved accuracy and robustness in graph-based algorithms.
