Deep Graph Networks

Graph-Induced Sum-Product Networks have been (finally!) accepted at ICLR 2024! Blog post coming in the following months… stay tuned! Special thanks to Mathias Niepert for his relentless support =).

We propose a probabilistic model that bridges graph machine learning and sum-product networks to tractably answer probabilistic queries. Our Graph-induced Sum-Product Network can solve unsupervised and supervised graph tasks and it is especially effective in modeling the missing data distribution as well as exploiting unlabeled data in a scarce supervision scenario.

PyDGN just got accepted in Journal of Open Source Software =) I have to say, compared to other prestigious journals, I got much more constructive feedback and the review process was faster and transparent.

I am really excited: my very first single-author paper has been accepted at NeurIPS 2023! I still can’t believe that I managed to pull this off, especially after submitting it for the first time =) give it a few weeks for the papers to be released on OpenReview!

Infinite Contextual Graph Markov Model

PyDGN 1.0.0 is out! We have added a documentation, refactored the code, implemented a bunch of user-friendly features and provided clean, well commented configuration files!

PyDGN v0.5.0 is out

Graph Mixture Density Network

Deep Bayesian Graph Networks & Low-resource Classification

Continual Learning for Graphs