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20172020
most citedConsistent Estimation in General Sublinear Preferential Attachment Trees

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

math.ST2020

Detecting a botnet in a network

Gianmarco Bet, Kay Bogerd, Rui M. Castro +1

We formalize the problem of detecting the presence of a botnet in a network as an hypothesis testing problem where we observe a single instance of a graph. The null hypothesis, cor…

cs.SI2019

Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs

Tony Gracious, Shubham Gupta, Arun Kanthali +2

Although static networks have been extensively studied in machine learning, data mining, and AI communities for many decades, the study of dynamic networks has recently taken cente…

cs.SI2019

Equipping SBMs with RBMs: An Explainable Approach for Analysis of Networks with Covariates

Shubham Gupta, Gururaj K., Ambedkar Dukkipati +1

Networks with node covariates offer two advantages to community detection methods, namely, (i) exploit covariates to improve the quality of communities, and more importantly, (ii)…

math.ST2019

Detecting a planted community in an inhomogeneous random graph

Kay Bogerd, Rui M. Castro, Remco van der Hofstad +1

We study the problem of detecting whether an inhomogeneous random graph contains a planted community. Specifically, we observe a single realization of a graph. Under the null hypot…

math.PR2018

Cliques in rank-1 random graphs: the role of inhomogeneity

Kay Bogerd, Rui M. Castro, Remco van der Hofstad

We study the asymptotic behavior of the clique number in rank-1 inhomogeneous random graphs, where edge probabilities between vertices are roughly proportional to the product of th…

math.ST20171 cited

Consistent Estimation in General Sublinear Preferential Attachment Trees

Fengnan Gao, Aad van der Vaart, Rui Castro +1

We propose an empirical estimator of the preferential attachment function in the setting of general preferential attachment trees. Using a supercritical continuous-time branchi…