248 citations · 292 across the 7 of their papers we have counts for
5 papers · 1 filter
Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Takanori Maehara, Hoang NT
Theoretical analyses for graph learning methods often assume a complete observation of the input graph. Such an assumption might not be useful for handling any-size graphs due to t…
Stacked Graph Filter
Hoang NT, Takanori Maehara, Tsuyoshi Murata
We study Graph Convolutional Networks (GCN) from the graph signal processing viewpoint by addressing a difference between learning graph filters with fully connected weights versus…
Graph Homomorphism Convolution
Hoang NT, Takanori Maehara
In this paper, we study the graph classification problem from the graph homomorphism perspective. We consider the homomorphisms from to , where is a graph of interest (e…
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…
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,…