Adaptive Diffusions for Scalable Learning over Graphs (MLG@KDD18)

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. 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.