no code implementations • 21 Dec 2023 • Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh-Tung Nguyen, Tsung-Han Yang, Vijay Prakash Dwivedi, Wei-Hsian Yin, Jeng Wei, Stefan Winkler
Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records.
1 code implementation • 18 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.
1 code implementation • 25 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.
2 code implementations • 16 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.
Ranked #3 on Link Prediction on PCQM-Contact
3 code implementations • 25 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.
Ranked #1 on Graph Property Prediction on ogbg-ppa
1 code implementation • ICLR 2022 • Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson
An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers.
Ranked #12 on Graph Regression on ZINC-500k
3 code implementations • 17 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.
16 code implementations • 2 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.
Ranked #1 on Link Prediction on COLLAB
1 code implementation • International Conference on Natural Language Processing (ICON) 2017, Kolkata, India 2017 • Vijay Prakash Dwivedi, Manish Shrivastava
Word embeddings are being used for several linguistic problems and NLP tasks.