Jure Leskovec

Network Enhancement improves the signal-to-noise ratio of weighted biological networks, enhancing downstream analysis performance across multiple applications.

This paper introduces a method to prioritize network communities for experimental validation, showing significant improvement in prioritization efficiency.

The paper introduces Decagon, a graph convolutional network that predicts polypharmacy side effects by modeling drug interactions and side effects as a multimodal graph, achieving up to 69% better performance than baselines.

The paper analyzes 519 disease pathways in the human interactome, finding that most do not form single connected components and that existing methods struggle with disconnected pathways.