Subgraph neural networks learn powerful subgraph representations that create fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.
GNNGuard can defend graph neural networks against a variety of training-time attacks. Remarkably, GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.
G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.
A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.
Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.
A new preprint describing a graph neural network approach for the prediction of molecular interactions, including drug-drug, drug-target, protein-protein, and gene-disease interactions.
The call for ECML-PKDD 2020 PhD Forum Track is now online. If you are a PhD student, submit your work on machine learning and knowledge discovery.
We are excited to share our latest results on how networks and graph machine-learning help us search for a cure for COVID-19.
We are joining AI Cures initiative at MIT! We will develop machine learning methods for finding promising antiviral molecules for COVID-19 and other emerging pathogens.
Haoxin, Michelle, and Xiang joined the lab. Welcome! We look forward to seeing you all in the lab!
A new preprint on meta learning for identifying and naming cell types, even cell types that have never been seen before and do not exist in the training data. Check it out!
Marinka is co-editing a special issue of ACM/IEEE TCBB on Deep learning and graph embeddings for network biology. Submit your finest work!
Kathleen, Jingyi, Kexin, Yujie, and Stone joined the lab. Welcome!