Search Results for author: Vijay Prakash Dwivedi

Found 9 papers, 8 papers with code

Graph Transformers for Large Graphs

1 code implementation18 Dec 2023 Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, Tong Zhao

As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.

Graph Learning Graph Property Prediction +3

Union Subgraph Neural Networks

1 code implementation25 May 2023 Jiaxing Xu, Aihu Zhang, Qingtian Bian, Vijay Prakash Dwivedi, Yiping Ke

We first investigate different kinds of connectivities existing in a local neighborhood and identify a substructure called union subgraph, which is able to capture the complete picture of the 1-hop neighborhood of an edge.

Computational Efficiency Graph Representation Learning

Long Range Graph Benchmark

2 code implementations16 Jun 2022 Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.

Benchmarking Graph Classification +4

Recipe for a General, Powerful, Scalable Graph Transformer

3 code implementations25 May 2022 Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini

We propose 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.

Graph Classification Graph Property Prediction +4

A Generalization of Transformer Networks to Graphs

3 code implementations17 Dec 2020 Vijay Prakash Dwivedi, Xavier Bresson

This work 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.

Graph Regression Inductive Bias +3

Benchmarking Graph Neural Networks

16 code implementations2 Mar 2020 Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Benchmarking Graph Classification +3

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