%0 Generic %1 Mucha2009 %A Mucha, Peter J. %A Richardson, Thomas %A Macon, Kevin %A Porter, Mason A. %A Onnela, Jukka-Pekka %D 2009 %K community data detection dynamic toread %T Community Structure in Time-Dependent, Multiscale, and Multiplex Networks %U http://arxiv.org/abs/0911.1824 %X During the last decade, the science of networks has grown into an enormous interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. One of the most important and prominent ideas from network science is the algorithmic detection of tightly-connected groups of nodes known as communities. Here we develop a formulation to detect communities in a very broad setting by studying general dynamical processes on networks. We create a new framework of network quality functions that allows us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through additional links that connect each node in one network slice to itself in other slices. This new framework allows one for the first time to study community structure in a very general setting that encompasses networks that evolve in time, have multiple types of ties (multiplexity), and have multiple scales.

@misc{Mucha2009, abstract = { During the last decade, the science of networks has grown into an enormous interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. One of the most important and prominent ideas from network science is the algorithmic detection of tightly-connected groups of nodes known as communities. Here we develop a formulation to detect communities in a very broad setting by studying general dynamical processes on networks. We create a new framework of network quality functions that allows us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through additional links that connect each node in one network slice to itself in other slices. This new framework allows one for the first time to study community structure in a very general setting that encompasses networks that evolve in time, have multiple types of ties (multiplexity), and have multiple scales. }, added-at = {2010-02-24T15:41:32.000+0100}, author = {Mucha, Peter J. and Richardson, Thomas and Macon, Kevin and Porter, Mason A. and Onnela, Jukka-Pekka}, biburl = {https://www.bibsonomy.org/bibtex/2e4bdff9d315195bc65a74a5242ad5b19/folke}, description = {[0911.1824] Community Structure in Time-Dependent, Multiscale, and Multiplex Networks}, interhash = {1e595ba6a001086edcadb46ccbb41f49}, intrahash = {e4bdff9d315195bc65a74a5242ad5b19}, keywords = {community data detection dynamic toread}, note = {cite arxiv:0911.1824 Comment: 23 pages, 3 figures, 1 table}, timestamp = {2010-02-24T15:41:32.000+0100}, title = {Community Structure in Time-Dependent, Multiscale, and Multiplex Networks}, url = {http://arxiv.org/abs/0911.1824}, year = 2009 }