no code implementations • 30 Sep 2020 • Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
In this work, we propose Uncertainty Matching GNN (UM-GNN), that is aimed at improving the robustness of GNN models, particularly against poisoning attacks to the graph structure, by leveraging epistemic uncertainties from the message passing framework.
no code implementations • 1 Nov 2018 • Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Andreas Spanias
Machine learning models that can exploit the inherent structure in data have gained prominence.
no code implementations • 2 Oct 2018 • Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
Though deep network embeddings, e. g. DeepWalk, are widely adopted for community discovery, we argue that feature learning with random node attributes, using graph neural networks, can be more effective.