4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Deep Graph Attention Networks
Jun Kato, Airi Mita, Keita Gobara +1
Graphs are useful for representing various realworld objects. However, graph neural networks (GNNs) tend to suffer from over-smoothing, where the representations of nodes of differ…
cs.SI2020★ 1 cited
Refining Similarity Matrices to Cluster Attributed Networks Accurately
Yuta Yajima, Akihiro Inokuchi
As a result of the recent popularity of social networks and the increase in the number of research papers published across all fields, attributed networks consisting of relationshi…
cs.DB2011★ 4 cited
GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search
Akihiro Inokuchi, Hiroaki Ikuta, Takashi Washio
The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequ…