My research interests span a wide array of topics around graph neural networks. Rather than focusing on a particular niche, I like to constantly look for new interesting and impactful problems. Some of the problems in graph neural networks that have caught my interest so far are:

Scalability: How do we scale GNNs to billion nodes graphs?

Dynamic Graphs: How do we learn on graphs that change over time?

Missing Node Features: How do apply GNNs to graphs where we only observe only a subset of features for each node (which is almost always the case in practice)?

Low Homophily Graphs: How do we design GNNs that work on graphs with low label homophily (where neighbors tend to have different labels)
Publications
Below is a list of my publications in reversed chronological order.
2021

On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
Rossi, Emanuele,
Kenlay, Henry,
Gorinova, Maria I.,
Chamberlain, Ben,
Dong, Xiaowen, and
Bronstein, Michael M.
ArXiv 2021

GRAND: Graph Neural Diffusion
Chamberlain, Ben,
Rowbottom, James,
Gorinova, Maria I.,
Webb, Stefan D.,
Rossi, Emanuele, and
Bronstein, Michael M.
Proceedings of the 38th International Conference on Machine Learning,
ICML 2021
2020

Tuning Word2vec for Large Scale Recommendation Systems
RecSys  14th ACM Conference on Recommender Systems 2020

Temporal Graph Networks for Deep Learning on Dynamic Graphs
Rossi, Emanuele,
Chamberlain, Ben,
Frasca, Fabrizio,
Eynard, Davide,
Monti, Federico, and
Bronstein, Michael
ICML Workshop on Graph Representation Learning 2020

SIGN: Scalable Inception Graph Neural Networks
Rossi, Emanuele,
Frasca, Fabrizio,
Chamberlain, Ben,
Eynard, Davide,
Bronstein, Michael, and
Monti, Federico
ICML Workshop on Graph Representation Learning 2020
2019

ncRNA Classification with Graph Convolutional Networks
KDD Workshop on Deep Learning on Graphs 2019