1 code implementation • 7 Dec 2021 • Vijay Lingam, Chanakya Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam
We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.
no code implementations • 29 Sep 2021 • Vijay Lingam, Chanakya Ajit Ekbote, Manan Sharma, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam
We study various aspects of our proposed model including, dependency on the number of eigencomponents utilized, latent polynomial filters learned, and performance of the individual polynomials on the node classification task.
no code implementations • 28 Jul 2021 • Vijay Lingam, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam
More recent approaches adapt the eigenvalues of the graph.
no code implementations • 24 Jun 2021 • Vijay Lingam, Rahul Ragesh, Arun Iyer, Sundararajan Sellamanickam
Our approach achieves up to ~30% improvement in performance over state-of-the-art methods on heterophilic graphs.
Ranked #2 on Node Classification on Crocodile
no code implementations • ICLR Workshop GTRL 2021 • Vijay Lingam, Arun Iyer, Rahul Ragesh
Graph Neural Networks have shown excellent performance on semi-supervised classification tasks.
no code implementations • 15 Feb 2021 • Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer, Ramakrishna Bairi
CF-LGCN-U models naturally possess the inductive capability for new items, and we propose a simple solution to generalize for new users.
no code implementations • 23 Oct 2020 • Arunava Chakraborty, Rahul Ragesh, Mahir Shah, Nipun Kwatra
We propose a framework for semi-supervised training of cGANs which utilizes sparse labels to learn the conditional mapping, and at the same time leverages a large amount of unsupervised data to learn the unconditional distribution.
no code implementations • 19 Aug 2020 • Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer, Ram Bairi, Vijay Lingam
We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features.
no code implementations • 8 Apr 2020 • Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam, Arun Iyer
Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification.