#### Filter Results:

- Full text PDF available (76)

#### Publication Year

2003

2017

- This year (5)
- Last 5 years (52)
- Last 10 years (71)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

#### Method

#### Organism

Learn More

- Aaron Clauset, Cosma Rohilla Shalizi, Mark E. J. Newman
- SIAM Review
- 2009

Aaron Clauset, 2 Cosma Rohilla Shalizi, and M. E. J. Newman Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA Department of Physics and Center for the Study of Complex Systems,… (More)

- Aaron Clauset, M E J Newman, Cristopher Moore
- Physical review. E, Statistical, nonlinear, and…
- 2004

The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many… (More)

- Aaron Clauset, Cristopher Moore, M E J Newman
- Nature
- 2008

Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups… (More)

- Benjamin H Good, Yves-Alexandre de Montjoye, Aaron Clauset
- Physical review. E, Statistical, nonlinear, and…
- 2010

Although widely used in practice, the behavior and accuracy of the popular module identification technique called modularity maximization is not well understood in practical contexts. Here, we present a broad characterization of its performance in such situations. First, we revisit and clarify the resolution limit phenomenon for modularity maximization.… (More)

- Aaron Clauset
- Physical review. E, Statistical, nonlinear, and…
- 2005

Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring… (More)

- Leto Peel, Daniel B. Larremore, Aaron Clauset
- Science advances
- 2017

Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection… (More)

- Pratha Sah, Lisa O. Singh, Aaron Clauset, Shweta Bansal
- BMC Bioinformatics
- 2013

Community structure is ubiquitous in biological networks. There has been an increased interest in unraveling the community structure of biological systems as it may provide important insights into a system’s functional components and the impact of local structures on dynamics at a global scale. Choosing an appropriate community detection algorithm to… (More)

- Leto Peel, Aaron Clauset
- AAAI
- 2015

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 of time. An important task in analyzing such evolving networks is change-point detection, in which we both identify the times at which the large-scale pattern of interactions… (More)

- Mark E. J. Newman, Aaron Clauset
- Nature communications
- 2016

For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this 'metadata' can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in… (More)

- Daniel B. Larremore, Aaron Clauset, Abigail Z. Jacobs
- Physical review. E, Statistical, nonlinear, and…
- 2014

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 exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of… (More)