248 citations · 292 across the 7 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.LG2019★ 14 cited
A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks
Takanori Maehara, Hoang NT
We present a simple proof for the universality of invariant and equivariant tensorized graph neural networks. Our approach considers a restricted intermediate hypothetical model na…
stat.ML2019★ 248 cited
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
Hoang NT, Takanori Maehara
Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed de…
cs.LG2019★ 23 cited
Learning Graph Neural Networks with Noisy Labels
Hoang NT, Choong Jun Jin, Tsuyoshi Murata
We study the robustness to symmetric label noise of GNNs training procedures. By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE,…