Adaptive Diffusions for Scalable Learning over Graphs
This addresses the challenge of scalable and accurate graph learning for applications like social networks or bioinformatics, though it is incremental as it builds on existing diffusion-based classifiers.
The paper tackles the problem of graph classification by introducing a method to learn class-specific diffusion functions adapted to network topology, which significantly improves performance over fixed diffusions and often surpasses computationally heavier state-of-the-art methods in accuracy.
Diffusion-based classifiers such as those relying on the Personalized PageRank and the Heat kernel, enjoy remarkable classification accuracy at modest computational requirements. Their performance however is affected by the extent to which the chosen diffusion captures a typically unknown label propagation mechanism, that can be specific to the underlying graph, and potentially different for each class. The present work introduces a disciplined, data-efficient approach to learning class-specific diffusion functions adapted to the underlying network topology. The novel learning approach leverages the notion of "landing probabilities" of class-specific random walks, which can be computed efficiently, thereby ensuring scalability to large graphs. This is supported by rigorous analysis of the properties of the model as well as the proposed algorithms. Furthermore, a robust version of the classifier facilitates learning even in noisy environments. Classification tests on real networks demonstrate that adapting the diffusion function to the given graph and observed labels, significantly improves the performance over fixed diffusions; reaching -- and many times surpassing -- the classification accuracy of computationally heavier state-of-the-art competing methods, that rely on node embeddings and deep neural networks.