248 citations · 292 across the 7 of their papers we have counts for
7 papers
Leaping Through Time with Gradient-based Adaptation for Recommendation
Nuttapong Chairatanakul, Hoang NT, Xin Liu +1
Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of t…
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…
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…
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,…