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20132025
most citedLink prediction for partially observed networks

6 citations · 9 across the 6 of their papers we have counts for

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5 papers · 1 filter

stat.ML2025

Variational Estimators for Node Popularity Models

Jony Karki, Dongzhou Huang, Yunpeng Zhao

Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipa…

stat.ML2022★ 1 cited

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

Yunpeng Zhao, Ning Hao, Ji Zhu

Bipartite graphs are ubiquitous across various scientific and engineering fields. Simultaneously grouping the two types of nodes in a bipartite graph via biclustering represents a…

stat.ML2018

Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways

Yunpeng Zhao, Qing Pan, Chengan Du

When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes…

stat.ML2017

On Consistency of Graph-based Semi-supervised Learning

Chengan Du, Yunpeng Zhao, Feng Wang

Graph-based semi-supervised learning is one of the most popular methods in machine learning. Some of its theoretical properties such as bounds for the generalization error and the…

stat.ML2013★ 6 cited

Link prediction for partially observed networks

Yunpeng Zhao, Elizaveta Levina, Ji Zhu

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative example…