7.7k citations · 24.5k across the 60 of their papers we have counts for
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Stacking Models for Nearly Optimal Link Prediction in Complex Networks
Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan +2
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and impro…
Evaluating Overfit and Underfit in Models of Network Community Structure
Amir Ghasemian, Homa Hosseinmardi, Aaron Clauset
A common data mining task on networks is community detection, which seeks an unsupervised decomposition of a network into structural groups based on statistical regularities in the…
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Amir Ghasemian, Pan Zhang, Aaron Clauset +2
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their communit…
A unified view of generative models for networks: models, methods, opportunities, and challenges
Abigail Z. Jacobs, Aaron Clauset
Research on probabilistic models of networks now spans a wide variety of fields, including physics, sociology, biology, statistics, and machine learning. These efforts have produce…
Learning Latent Block Structure in Weighted Networks
Christopher Aicher, Abigail Z. Jacobs, Aaron Clauset
Community detection is an important task in network analysis, in which we aim to learn a network partition that groups together vertices with similar community-level connectivity p…
Adapting the Stochastic Block Model to Edge-Weighted Networks
Christopher Aicher, Abigail Z. Jacobs, Aaron Clauset
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution. This generalization introduc…