Robust Detection of Dynamic Community Structure in Networks

By D. Bassett, M. A. Porter, Nicholas F. Wymbs, Scott T. Grafton, Jean M. Carlson, Peter J. Mucha. Chaos, 2012

481 citations recorded by the source corpus.

We describe techniques for the robust detection of community structure in some classes of time-dependent networks. Specifically, we consider the use of statistical null models for facilitating the principled identification of structural modules in semi-decomposable systems. Null models play an important role both in the optimization of quality functions such as modularity and in the subsequent assessment of the statistical validity of identified community structure. We examine the sensitivity of such methods to model parameters and show how comparisons to null models can help identify system scales. By considering a large number of optimizations, we quantify the variance of network diagnostics over optimizations ("optimization variance") and over randomizations of network structure ("randomization variance"). Because the modularity quality function typically has a large number of nearly degenerate local optima for networks constructed using real data, we develop a method to construct representative partitions that uses a null model to correct for statistical noise in sets of partitions. To illustrate our results, we employ ensembles of time-dependent networks extracted from both nonlinear oscillators and empirical neuroscience data.

Subfields: Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/1f3c6f2c8843f401eaceac551ce51adfe3e1c2ec

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In Cognitive Neuroscience

In Statistical and Nonlinear Physics

Citations in the corpus

Modular Brain Networks (2015, methodology): “To resolve this variability, we performed consensus clustering, following Bassett et al. (2013) and Lancichinetti & Fortunato (2012).”
Network-Level Structure-Function Relationships in Human Neocortex (2016, methodology): “Rather than treat anyof the 250 individual partitions as representative,we derived a consensus partition using the method presented in Bassett et al. (2013).”
Task-Based Core-Periphery Organization of Human Brain Dynamics (2012, methodology): “We employ computational tools for dynamic community detection [14,15] for multilayer representations of temporal networks [16] and summarize our findings using diagnostics that”
Mathematical Formulation of Multilayer Networks (2013, background): “[13,41,84] give special cases of the multilayer modularity in Eq.”
Multilayer networks (2013, background): “as the qualitative behaviour of time-series output of coupled nonlinear oscillators [103,302].”
Random walks and diffusion on networks (2016, background): “[343, 344, 355, 371] for illustrations and discussions.”
Multilayer Analysis and Visualization of Networks (2014, background): “communities) that are sparsely connected to each other when compared with some multilayer random-graph (null) model [41,42,60].”
Intrinsic and task-evoked network architectures of the human brain. (2014, unknown): “To identify a representative partition, several consensus algorithms exist (Bassett et al., 2013a; Doron et al., 2012; Lancichinetti and Fortunato, 2012).”
Controllability of structural brain networks (2014, unknown): “First, we compute a robust partition of the brain network as described in 49 , and we identify the set of N 1 boundary nodes.”