 A Comprehensive Survey on Distributed Training of Graph Neural NetworksProceedings of the IEEE (Proc. IEEE), 2022 |
 Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural
NetworksIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022 |
 FunQG: Molecular Representation Learning Via Quotient GraphsJournal of Chemical Information and Modeling (JCIM), 2022 |
 Measuring and Sampling: A Metric-guided Subgraph Learning Framework for
Graph Neural NetworkInternational Journal of Intelligent Systems (IJIS), 2021 |
 Scalable Graph Neural Network Training: The Case for SamplingACM SIGOPS Operating Systems Review (OSR), 2021 |
 Sampling methods for efficient training of graph convolutional networks:
A surveyIEEE/CAA Journal of Automatica Sinica (IEEE/CAA J. Autom. Sinica), 2021 |
 An efficient manifold density estimator for all recommendation systemsInternational Conference on Neural Information Processing (ICONIP), 2020 |
