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Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
A generalized framework of network quality functions was developed that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices.
Dynamic reconfiguration of human brain networks during learning
- D. Bassett, N. Wymbs, M. Porter, P. Mucha, J. Carlson, Scott T. Grafton
- BiologyProceedings of the National Academy of Sciences
- 19 October 2010
This work investigates the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales and develops a general statistical framework for the identification of modular architectures in evolving systems.
- Mikko Kivelä, A. Arenas, M. Barthelemy, J. Gleeson, Y. Moreno, M. Porter
- Computer ScienceJ. Complex Networks
- 27 September 2013
This chapter shows how interconnected multilayer topology describes such networks more accurately than edge coloring does and introduces the tensor formalism used to construct them.
Social Structure of Facebook Networks
The social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time is studied, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions.
Comparing Community Structure to Characteristics in Online Collegiate Social Networks
This study examines the importance of common high school affiliation at large state universities and the varying degrees of influence that common major can have on the social structure at different universities, indicating that university networks typically have multiple organizing factors rather than a single dominant one.
Mathematical Formulation of Multilayer Networks
This paper introduces a tensorial framework to study multilayer networks, and discusses the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvectorcentrality, modularity, von Neumann entropy, and diffusion—for this framework.
Communities in Networks
To help ease newcomers into the field, a guide to available methodology and open problems is provided, and why scientists from diverse backgrounds are interested in community detection is discussed.
A roadmap for the computation of persistent homology
- N. Otter, M. Porter, U. Tillmann, P. Grindrod, Heather A. Harrington
- Computer ScienceEPJ Data Science
- 30 June 2015
A friendly introduction to PH is given, the pipeline for the computation of PH is navigated with an eye towards applications, and a range of synthetic and real-world data sets are used to evaluate currently available open-source implementations for the computations of PH.
Robust Detection of Dynamic Community Structure in Networks
- D. Bassett, M. Porter, N. Wymbs, Scott T. Grafton, J. Carlson, P. Mucha
- Computer ScienceChaos
- 19 June 2012
This work considers the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems and develops a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions.