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)


Below is a list of my publications in reversed chronological order.


  1. 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 [arXiv] [Blog Post] [Code]


  1. Tuning Word2vec for Large Scale Recommendation Systems Chamberlain, Ben, Rossi, Emanuele, Shiebler, Dan, Sedhain, Suvash, and Bronstein, Michael RecSys - 14th ACM Conference on Recommender Systems 2020 [arXiv]
  2. 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 [arXiv] [Blog Post] [Slides] [Code]
  3. 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 [arXiv] [Blog Post] [Code]


  1. ncRNA Classification with Graph Convolutional Networks Rossi, Emanuele, Monti, Federico, Bronstein, Michael, and LiĆ², Pietro KDD Workshop on Deep Learning on Graphs 2019 [arXiv] [Code]