19 citations · 36 across the 8 of their papers we have counts for
6 papers · 1 filter
Probing Graph Representations
Mohammad Sadegh Akhondzadeh, Vijay Lingam, Aleksandar Bojchevski
Today we have a good theoretical understanding of the representational power of Graph Neural Networks (GNNs). For example, their limitations have been characterized in relation to…
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.…
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…