Factorized Graph Neural Networks - With Some Algebraic Cheating

Explore node classification in graph data management through a 29-minute lecture by Wolfgang Gatterbauer from Northeastern University. Delve into a novel method called distant compatibility estimation, which effectively labels sparsely labeled graphs. Learn how this approach creates factorized graph representations and performs estimation on smaller graph sketches. Discover the concept of algebraic amplification and its application in leveraging algebraic properties to amplify sparse signals. Compare the efficiency of this estimator to traditional hold-out based approaches and examine its impact on end-to-end classification accuracy. Gain insights from related research presented at SIGMOD 2020 and VLDB 2015, focusing on factorized graph representations and linearized belief propagation.

Factorized Graph Neural Networks (With Some Algebraic Cheating)