activity
20172022
most citedLine Hypergraph Convolution Network: Applying Graph Convolution for Hypergraphs

21 citations · 58 across the 9 of their papers we have counts for

collaborators

14 papers

cs.IR2022★ 16 cited

Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

Chandan K. Reddy, Lluís Màrquez, Fran Valero +6

Improving the quality of search results can significantly enhance users experience and engagement with search engines. In spite of several recent advancements in the fields of mach…

cs.SE2021

Monolith to Microservices: Representing Application Software through Heterogeneous Graph Neural Network

Alex Mathai, Sambaran Bandyopadhyay, Utkarsh Desai +1

Monolithic software encapsulates all functional capabilities into a single deployable unit. But managing it becomes harder as the demand for new functionalities grow. Microservice…

cs.SE2021★ 4 cited

Graph Neural Network to Dilute Outliers for Refactoring Monolith Application

Utkarsh Desai, Sambaran Bandyopadhyay, Srikanth Tamilselvam

Microservices are becoming the defacto design choice for software architecture. It involves partitioning the software components into finer modules such that the development can ha…

cs.LG2020

Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture

Anoushka Vyas, Sambaran Bandyopadhyay

Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the…

cs.SI2020

Unsupervised Constrained Community Detection via Self-Expressive Graph Neural Network

Sambaran Bandyopadhyay, Vishal Peter

Graph neural networks (GNNs) are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction. Comparatively lesser work…

cs.SI2020★ 4 cited

Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description

Sambaran Bandyopadhyay, Saley Vishal Vivek, M. N. Murty

Network (or graph) embedding is the task to map the nodes of a graph to a lower dimensional vector space, such that it preserves the graph properties and facilitates the downstream…