CMSA New Technologies in Mathematics: Subgraph Representation Learning
Marinka Zitnik - Department of Biomedical Informatics, Harvard
Graph representation learning has emerged as a dominant paradigm for networked data. Still, prevailing methods require abundant label information and focus on representations of nodes, edges, or entire graphs. While graph-level representations provide overarching views of graphs, they do so at the loss of finer local structure. In contrast, node-level representations preserve local topological structures, potentially to the detriment of the big picture. In this talk, I will discuss how subgraph representations are critical to advance today's methods. First, I will outline Sub-GNN, the first subgraph neural network to learn disentangled subgraph representations. Second, I will describe G-Meta, a novel meta-learning approach for graphs. G-Meta uses subgraphs to adapt to a new task using only a handful of nodes or edges. G-Meta is theoretically justified, and remarkably, can learn in most challenging, few-shot settings that require generalization to completely new graphs and never-before-seen labels. Finally, I will discuss applications in biology and medicine. The new methods have enabled the repurposing of drugs for new diseases, including COVID-19, where predictions were experimentally verified in the wet laboratory. Further, the methods identified drug combinations safer for patients than previous treatments and provided accurate predictions that can be interpreted meaningfully.