V
Vijay Prakash Dwivedi
Researcher at Nanyang Technological University
Publications - 10
Citations - 1210
Vijay Prakash Dwivedi is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 5 publications receiving 254 citations. Previous affiliations of Vijay Prakash Dwivedi include Motilal Nehru National Institute of Technology Allahabad.
Papers
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Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
Journal Article
Benchmarking Graph Neural Networks
TL;DR: A reproducible GNN benchmarking framework is introduced, with the facility for researchers to add new models conveniently for arbitrary datasets, and a principled investigation into the recent Weisfeiler-Lehman GNNs (WL-GNNs) compared to message passing-based graph convolutional networks (GCNs).
Posted Content
A Generalization of Transformer Networks to Graphs
TL;DR: A graph transformer with four new properties compared to the standard model, which closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs.
Proceedings ArticleDOI
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek,Michael Galkin,Vijay Prakash Dwivedi,Anh Tuan Luu,Guy Wolf,Dominique Beaini +5 more
TL;DR: A recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks is proposed and a modular framework that supports multiple types of encodings and that provides scalability both in small and large graphs is built.
Proceedings ArticleDOI
Long Range Graph Benchmark
Vijay Prakash Dwivedi,Ladislav Rampášek,Michael Galkin,Alipanah Parviz,Guy Wolf,Anh Tuan Luu,Dominique Beaini +6 more
TL;DR: The Long Range Graph Benchmark (LRGB) 1 is presented with 5 graph learning datasets that arguably require LRI reasoning to achieve strong performance in a given task and is suitable for benchmarking and exploration of MP-GNNs and Graph Transformer architectures that are intended to capture LRI.