7.7k citations · 24.5k across the 60 of their papers we have counts for
5 papers · 1 filter
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…
Efficiently inferring community structure in bipartite networks
Daniel B. Larremore, Aaron Clauset, Abigail Z. Jacobs
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhib…
Detecting change points in the large-scale structure of evolving networks
Leto Peel, Aaron Clauset
Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period…
Rejoinder of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard
Aaron Clauset, Ryan Woodard
Rejoinder of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard [arXiv:1209.0089].