GNN Transformation Framework for Improving Efficiency and Scalability

1 code implementation • 25 Jul 2022 • Seiji Maekawa, Yuya Sasaki, George Fletcher, Makoto Onizuka

We propose a framework that automatically transforms non-scalable GNNs into precomputation-based GNNs which are efficient and scalable for large-scale graphs.

Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs

1 code implementation • 18 Jun 2022 • Seiji Maekawa, Koki Noda, Yuya Sasaki, Makoto Onizuka

We hope this work offers interesting insights for future research.

Node Classification

Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning

1 code implementation • 21 Sep 2018 • Seiji Maekawa, Koh Takeuch, Makoto Onizuka

We consider the clustering problem of attributed graphs.

Graph Clustering