10 citations · 47 across the 16 of their papers we have counts for
7 papers · 1 filter
Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap
Indrila Ganguly, Srijan Sengupta, Sujit Ghosh
Residual bootstrap is a classical method for statistical inference in regression settings. With massive data sets becoming increasingly common, there is a demand for computationall…
Word Embeddings as Statistical Estimators
Neil Dey, Matthew Singer, Jonathan P. Williams +1
Word embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets,…
Core-periphery structure in networks: a statistical exposition
Eric Yanchenko, Srijan Sengupta
Many real-world networks are theorized to have core-periphery structure consisting of a densely-connected core and a loosely-connected periphery. While this phenomenon has been ext…
Testing for the Network Small-World Property
Kartik Lovekar, Srijan Sengupta, Subhadeep Paul
Researchers have long observed that the ``small-world" property, which combines the concepts of high transitivity or clustering with a low average path length, is ubiquitous for ne…
A Bootstrap-based Method for Testing Network Similarity
Somnath Bhadra, Kaustav Chakraborty, Srijan Sengupta +1
This paper studies the matched network inference problem, where the goal is to determine if two networks, defined on a common set of nodes, exhibit a specific form of stochastic si…
Detecting and Localizing Anomalous Cliques in Inhomogeneous Networks using Egonets
Subhankar Bhadra, Srijan Sengupta
Cliques, or fully connected subgraphs, are among the most important and well-studied graph motifs in network science. We consider the problem of finding a statisti- cally anomalous…