7 citations · 12 across the 15 of their papers we have counts for
4 papers · 1 filter
A Simple and Scalable Graph Neural Network for Large Directed Graphs
Seiji Maekawa, Yuya Sasaki, Makoto Onizuka
Node classification is one of the hottest tasks in graph analysis. Though existing studies have explored various node representations in directed and undirected graphs, they have o…
GNN Transformation Framework for Improving Efficiency and Scalability
Seiji Maekawa, Yuya Sasaki, George Fletcher +1
We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs. The advantages of our…
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs
Seiji Maekawa, Koki Noda, Yuya Sasaki +1
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the broad interest in developing and evaluating GNNs, they have been assessed with l…
Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning
Seiji Maekawa, Koh Takeuch, Makoto Onizuka
We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relation…