"An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation" accepted at DLGMA 2020 / AAAI 2020

The following paper has been accepted at The First International Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA’20) which will be held in conjuction with The Thirty Forth AAAI Conference on Artificial Intelligence 2020 in New York, NY, USA, February 7-12, 2020:

"An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation" by Nezihe Merve Gürel (ETH Zurich), Hansheng Ren (Microsoft Research), Yujing Wang (Microsoft Research), Hui Xue (Microsoft Research), Yaming Yang (Microsoft Research) and Ce Zhang (ETH Zurich).