203 citations · 225 across the 6 of their papers we have counts for
5 papers · 1 filter
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks
Abdalgader Abubaker, Takanori Maehara, Madhav Nimishakavi +1
Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. However, most of these meth…
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav +3
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise. Hypergraphs provide a flexible…
Lovasz Convolutional Networks
Prateek Yadav, Madhav Nimishakavi, Naganand Yadati +3
Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods hav…
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
Madhav Nimishakavi, Bamdev Mishra, Manish Gupta +1
Low rank tensor completion is a well studied problem and has applications in various fields. However, in many real world applications the data is dynamic, i.e., new data arrives at…
A dual framework for low-rank tensor completion
Madhav Nimishakavi, Pratik Jawanpuria, Bamdev Mishra
One of the popular approaches for low-rank tensor completion is to use the latent trace norm regularization. However, most existing works in this direction learn a sparse combinati…