Three new graph robustness papers at NeurIPS

Hi!

Thanks again for maintaining this valuable resource.

We have three new NeurIPS papers on (provably) robust learning on graphs and wanted to ask if we could include them.

In this paper, we revisit the problem of proving robustness for graphs from a perspective of geometric machine learning. Among other things, we show that proving robustness w.r.t. graph edit distance is actually not as hard as one might think.

(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More https://openreview.net/forum?id=mLe63bAYc7 Code will be made available here: https://www.cs.cit.tum.de/daml/equivariance-robustness/

In this paper, we propose a procedure to prove robustness to adversaries that only control a limited number of nodes and only a limited fraction of their features / edges.

Hierarchical Randomized Smoothing https://openreview.net/forum?id=6IhNHKyuJO Code will be made available here: https://www.cs.cit.tum.de/daml/hierarchical-smoothing

In this paper, we revisit adversarial training for graph neural networks.

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions https://openreview.net/forum?id=GPtroppvUM Code will be made available here: https://www.cs.cit.tum.de/daml/adversarial-training/

Thanks again, Jan

EdisonLeeeee / Graph-Adversarial-Learning

Three new graph robustness papers at NeurIPS #4