Adaptive Diffusions for Scalable Learning over Graphs

Abstract

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.

References in corpus (9)

• DeepWalk: Online Learning of Social Representations
• Semi-Supervised Classification with Graph Convolutional Networks
• Discrete Signal Processing on Graphs
• Diffusion-Convolutional Neural Networks
• Block Models and Personalized PageRank
• From Sparse Signals to Sparse Residuals for Robust Sensing
• Semi-supervised learning with max-margin graph cuts
• Detecting Overlapping Communities from Local Spectral Subspaces
• Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity

Cited by in corpus (9)

• Adaptive Universal Generalized PageRank Graph Neural Network
• Online Graph-Adaptive Learning with Scalability and Privacy
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning
• Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
• Improving Graph Neural Networks with Simple Architecture Design
• Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks
• Multiple Kernel Representation Learning on Networks
• Learning Optimal Graph Filters for Clustering of Attributed Graphs
• Random Surfing Revisited: Generalizing PageRank's Teleportation Model