203 citations · 225 across the 6 of their papers we have counts for
7 papers
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…
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Madhav Nimishakavi, Partha Talukdar
Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused onl…
Relation Schema Induction using Tensor Factorization with Side Information
Madhav Nimishakavi, Uday Singh Saini, Partha Talukdar
Given a set of documents from a specific domain (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identification of…