Communities, modules and large-scale structure in networks

By Mark Newman. Nature Physics, 2011

814 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/aeb923944b70ae924718dc3ac48aeaef7f68f5ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Hierarchical block structures and high-resolution model selection in large networks (2013, methodology): “In Appendix B we also include a comparison of the method with other algorithms for community detection that are not based on statistical inference.”
Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “Here, we explain and illustrate the commonly used modularity maximization algorithm 116 , which identi ﬁ es network modules with the maximum modularity coef ﬁ cients by Eq.”
Community detection and stochastic block models: recent developments (2017, background): “The results give about 95% agreement on the blogs’ political inclinations (which is roughly the state-of-the-art [133, 101, 80]).”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “It then partitions the cells by detecting communities 124 in the graph—groups of nodes that are more densely connected than expected by chance in the same graph; the partition’s modularity 125 is maximized by an efficient greedy heuristic 123 .”
Disentangling social networks from spatiotemporal dynamics: the temporal structure of a dolphin society (2012, background): “2007), a largescale level of complexity within the networked system (Newman 2011).”
Multi-scale brain networks (2016, neutral): “In the context of networks, communities refer to subnetworks (clusters of nodes and their edges) that are internally dense (many within-community edges) and externally sparse (few betweencommunity edges) (Porter et al., 2009; Newman, 2012).”
Generative models of the human connectome (2015, neutral): “Alternatively, it is possible to describe a network’s ”large-scale structure” (Newman, 2012) - i.e. its organization at an intermediate scale.”
Community detection in networks: A user guide (2016, background): “(14) (Nadakuditi and Newman, 2012), provided the network is not too sparse.”