We are happy to announce that our PhD student Franziska Heeg has been invited to present her recent NeurIPS paper at the "Workshop on Temporal and Dynamic Interactions" held at the Mathematical Institute of University of Oxford!

Over the past two decades. network science has developed statistical methods to analyze and model patterns in complex networks. Similarly, the deep learning community has recently developed new approaches to generalize neural network architectures to graph-structured data. Unfortunately, there are few interactions between these two communities. In a new preprint, we highlight challenges of opportunites at the intersection between network science and deep graph learning.

We a delighted to announce that two of our works have been accepted for the main research track of the Conference on Neural Information Processing Systems (NeurIPS), which is the world's premiere venue on deep learning. Congratulations to Christopher Blöcker, Chester Tan and Franziska Heeg!