Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

Moujahid, Abdelmalik; D’Anjou, Alicia; Cases, Blanca

doi:10.1016/j.chaos.2012.06.007

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…Another option would therefore be to employ asymmetrical measures, such as Granger and/or partial directed coherence, to construct effective connectivity models and determine whether healthy controls and patients showed different causal association resting measurements. Moreover, although our selection of coherence thresholds is consistent with one of the more common procedures in the literature, our thresholds could instead be determined by parameterless approaches derived from adaptive neighborhood algorithms (Moujahid et al, 2012). …”

Section: Discussionmentioning

confidence: 99%

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

Rutter¹,

Nadar²,

Holroyd³

et al. 2013

Front. Comput. Neurosci.

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Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.

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Section: Discussionmentioning

confidence: 99%

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

Rutter¹,

Nadar²,

Holroyd³

et al. 2013

Front. Comput. Neurosci.

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“…A parameterless mechanism adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. [27] In Ref. [28], a generalized Kuramoto model is considered, the phases of the nodes are naturally separated into several clusters after a period of evolution, and each cluster corresponds to a community in the network.…”

Section: Introductionmentioning

confidence: 99%

Dynamic evolutionary community detection algorithms based on the modularity matrix

Chen¹,

Zhang²,

Wang³

et al. 2014

Chinese Phys. B

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Motivated by the relationship of the dynamic behaviors and network structure, in this paper, we present two efficient dynamic community detection algorithms. The phases of the nodes in the network can evolve according to our proposed differential equations. In each iteration, the phases of the nodes are controlled by several parameters. It is found that the phases of the nodes are ultimately clustered into several communities after a short period of evolution. They can be adopted to detect the communities successfully. The second differential equation can dynamically adjust several parameters, so it can obtain satisfactory detection results. Simulations on some test networks have verified the efficiency of the presented algorithms.

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Assessing climate vulnerability and nonlinear rainfall dynamics in complex networks

Tongal

2024

Stoch Environ Res Risk Assess

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Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

Cited by 3 publications

References 30 publications

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks

Dynamic evolutionary community detection algorithms based on the modularity matrix

Assessing climate vulnerability and nonlinear rainfall dynamics in complex networks

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