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Archaeology of random recursive dags and cooper-frieze random networks
We study the problem of finding the root vertex in large growing networks. We prove that it is
possible to construct confidence sets of size independent of the number of vertices in the …
possible to construct confidence sets of size independent of the number of vertices in the …
Estimating the history of a random recursive tree
This paper studies the problem of estimating the order of arrival of the vertices in a random
recursive tree. Specifically, we study two fundamental models: the uniform attachment model …
recursive tree. Specifically, we study two fundamental models: the uniform attachment model …
Diffusion source identification on networks with statistical confidence
Diffusion source identification on networks is a problem of fundamental importance in a
broad class of applications, including controlling the spreading of rumors on social media …
broad class of applications, including controlling the spreading of rumors on social media …
Root estimation in Galton–Watson trees
Given only the free‐tree structure of a tree, the root estimation problem asks if one can guess
which of the free tree's nodes is the root of the original tree. We determine the maximum …
which of the free tree's nodes is the root of the original tree. We determine the maximum …
Seconder of the vote of thanks to Crane and Xu and contribution to the Discussion of 'Root and community inference on the latent growth process of a network'
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …
Yicong Jiang and Zheng Tracy Ke's contribution to the Discussion of 'Root and community inference on the latent growth process of a network'by Crane and Xu
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …
Fan Wang, Yi Yu and Alessandro Rinaldo's contribution to the Discussion of 'Root and community inference on the latent growth process of a network'by Crane and Xu
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …
Tianxi Li's contribution to the Discussion of 'Root and community inference on the latent growth process of a network'by Crane and Xu
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …
Qing Yang and **n Tong's contribution to the Discussion of 'Root and community inference on the latent growth process of a network'by Crane and Xu
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …
Jason Wyse, James Ng, Arthur White and Michael Fop's contribution to the Discussion of 'Root and community inference on the latent growth process of a network'by …
Many statistical models for networks overlook the fact that most real-world networks are
formed through a growth process. To address this, we introduce the Preferential Attachment …
formed through a growth process. To address this, we introduce the Preferential Attachment …