19 citations · 34 across the 8 of their papers we have counts for
8 papers
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…
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…
GLAM: Graph Learning by Modeling Affinity to Labeled Nodes for Graph Neural Networks
Vijay Lingam, Arun Iyer, Rahul Ragesh
Graph Neural Networks have shown excellent performance on semi-supervised classification tasks. However, they assume access to a graph that may not be often available in practice.…
User Embedding based Neighborhood Aggregation Method for Inductive Recommendation
Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam +2
We consider the problem of learning latent features (aka embedding) for users and items in a recommendation setting. Given only a user-item interaction graph, the goal is to recomm…
S2cGAN: Semi-Supervised Training of Conditional GANs with Fewer Labels
Arunava Chakraborty, Rahul Ragesh, Mahir Shah +1
Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they…
HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification
Rahul Ragesh, Sundararajan Sellamanickam, Arun Iyer +2
We consider the problem of learning efficient and inductive graph convolutional networks for text classification with a large number of examples and features. Existing state-of-the…
A Graph Convolutional Network Composition Framework for Semi-supervised Classification
Rahul Ragesh, Sundararajan Sellamanickam, Vijay Lingam +1
Graph convolutional networks (GCNs) have gained popularity due to high performance achievable on several downstream tasks including node classification. Several architectural varia…