24 citations · 62 across the 18 of their papers we have counts for
6 papers · 1 filter
Generative Models and Learning Algorithms for Core-Periphery Structured Graphs
Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
We consider core-periphery structured graphs, which are graphs with a group of densely and sparsely connected nodes, respectively, referred to as core and periphery nodes. The so-c…
Learning Sparse Graphs with a Core-periphery Structure
Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
In this paper, we focus on learning sparse graphs with a core-periphery structure. We propose a generative model for data associated with core-periphery structured networks to mode…
Product Graph Learning from Multi-domain Data with Sparsity and Rank Constraints
Sai Kiran Kadambari, Sundeep Prabhakar Chepuri
In this paper, we focus on learning product graphs from multi-domain data. We assume that the product graph is formed by the Cartesian product of two smaller graphs, which we refer…
Dr-COVID: Graph Neural Networks for SARS-CoV-2 Drug Repurposing
Siddhant Doshi, Sundeep Prabhakar Chepuri
The 2019 novel coronavirus (SARS-CoV-2) pandemic has resulted in more than a million deaths, high morbidities, and economic distress worldwide. There is an urgent need to identify…
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri +2
We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-bas…
Learning Multi-layer Graphs and a Common Representation for Clustering
Sravanthi Gurugubelli, Sundeep Prabhakar Chepuri
In this paper, we focus on graph learning from multi-view data of shared entities for spectral clustering. We can explain interactions between the entities in multi-view data using…