19 citations · 45 across the 10 of their papers we have counts for
12 papers
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
Chanakya Ekbote, Ajinkya Pankaj Deshpande, Arun Iyer +2
Unsupervised node representations learnt using contrastive learning-based methods have shown good performance on downstream tasks. However, these methods rely on augmentations that…
Simulating Network Paths with Recurrent Buffering Units
Divyam Anshumaan, Sriram Balasubramanian, Shubham Tiwari +3
Simulating physical network paths (e.g., Internet) is a cornerstone research problem in the emerging sub-field of AI-for-networking. We seek a model that generates end-to-end packe…
A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
Vijay Lingam, Chanakya Ekbote, Manan Sharma +3
Graph Neural Networks (GNNs) exploit signals from node features and the input graph topology to improve node classification task performance. However, these models tend to perform…
IGLU: Efficient GCN Training via Lazy Updates
S Deepak Narayanan, Aditya Sinha, Prateek Jain +2
Training multi-layer Graph Convolution Networks (GCN) using standard SGD techniques scales poorly as each descent step ends up updating node embeddings for a large portion of the g…
Effective Eigendecomposition based Graph Adaptation for Heterophilic Networks
Vijay Lingam, Rahul Ragesh, Arun Iyer +1
Graph Neural Networks (GNNs) exhibit excellent performance when graphs have strong homophily property, i.e. connected nodes have the same labels. However, they perform poorly on he…
Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs
Vijay Lingam, Rahul Ragesh, Arun Iyer +1
Graph Neural Networks (GNNs) have shown excellent performance on graphs that exhibit strong homophily with respect to the node labels i.e. connected nodes have same labels. However…