activity
20152023
most citedRevisiting Graph Neural Networks: All We Have is Low-Pass Filters

248 citations · 332 across the 19 of their papers we have counts for

collaborators

33 papers

cs.LG2023

Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks

Abdalgader Abubaker, Takanori Maehara, Madhav Nimishakavi +1

Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these meth…

cs.LG2021

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…

cs.LG2021

Abelian Neural Networks

Kenshin Abe, Takanori Maehara, Issei Sato

We study the problem of modeling a binary operation that satisfies some algebraic requirements. We first construct a neural network architecture for Abelian group operations and de…

cs.DS2020

r-Gathering Problems on Spiders:Hardness, FPT Algorithms, and PTASes

Soh Kumabe, Takanori Maehara

We consider the min-max -gathering problem described as follows: We are given a set of users and facilities in a metric space. We open some of the facilities and assign each use…

cs.LG2020★ 2 cited

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…

math.CO2020

Rank axiom of modular supermatroids: A connection with directional DR submodular functions

Takanori Maehara, So Nakashima

A matroid has been one of the most important combinatorial structures since it was introduced by Whitney as an abstraction of linear independence. As an important property of a mat…