Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Flow stability for dynamic community detection.

ABSTRACT: Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in these systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that a stationary state of the dynamics does not always exist. Here, we derive a method based on a dynamical process evolving on the temporal network. Our method allows dynamics that do not reach a steady state and uncovers two sets of communities for a given time interval that accounts for the ordering of edges in forward and backward time. We show that our method provides a natural way to disentangle the different dynamical scales present in a system with synthetic and real-world examples.

SUBMITTER: Bovet A

PROVIDER: S-EPMC9094665 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Flow stability for dynamic community detection.

Bovet Alexandre A Delvenne Jean-Charles JC Lambiotte Renaud R

Science advances 20220511 19

Many systems exhibit complex temporal dynamics due to the presence of different processes taking place simultaneously. An important task in these systems is to extract a simplified view of their time-dependent network of interactions. Community detection in temporal networks usually relies on aggregation over time windows or consider sequences of different stationary epochs. For dynamics-based methods, attempts to generalize static-network methodologies also face the fundamental difficulty that ...[more]

PMID: 35544564

Similar Datasets

Robust detection of dynamic community structure in networks.

Project description: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.

| S-EPMC3618100 | biostudies-other

Community Detection on Networks with Ricci Flow.

Project description:Many complex networks in the real world have community structures - groups of well-connected nodes with important functional roles. It has been well recognized that the identification of communities bears numerous practical applications. While existing approaches mainly apply statistical or graph theoretical/combinatorial methods for community detection, in this paper, we present a novel geometric approach which enables us to borrow powerful classical geometric methods and properties. By considering networks as geometric objects and communities in a network as a geometric decomposition, we apply curvature and discrete Ricci flow, which have been used to decompose smooth manifolds with astonishing successes in mathematics, to break down communities in networks. We tested our method on networks with ground-truth community structures, and experimentally confirmed the effectiveness of this geometric approach.

| S-EPMC6620345 | biostudies-literature

Dynamic graphs, community detection, and Riemannian geometry.

Project description:A community is a subset of a wider network where the members of that subset are more strongly connected to each other than they are to the rest of the network. In this paper, we consider the problem of identifying and tracking communities in graphs that change over time - dynamic community detection - and present a framework based on Riemannian geometry to aid in this task. Our framework currently supports several important operations such as interpolating between and averaging over graph snapshots. We compare these Riemannian methods with entry-wise linear interpolation and find that the Riemannian methods are generally better suited to dynamic community detection. Next steps with the Riemannian framework include producing a Riemannian least-squares regression method for working with noisy data and developing support methods, such as spectral sparsification, to improve the scalability of our current methods.

| S-EPMC6214282 | biostudies-literature

BoCluSt: Bootstrap Clustering Stability Algorithm for Community Detection.

Project description:The identification of modules or communities in sets of related variables is a key step in the analysis and modeling of biological systems. Procedures for this identification are usually designed to allow fast analyses of very large datasets and may produce suboptimal results when these sets are of a small to moderate size. This article introduces BoCluSt, a new, somewhat more computationally intensive, community detection procedure that is based on combining a clustering algorithm with a measure of stability under bootstrap resampling. Both computer simulation and analyses of experimental data showed that BoCluSt can outperform current procedures in the identification of multiple modules in data sets with a moderate number of variables. In addition, the procedure provides users with a null distribution of results to evaluate the support for the existence of community structure in the data. BoCluSt takes individual measures for a set of variables as input, and may be a valuable and robust exploratory tool of network analysis, as it provides 1) an estimation of the best partition of variables into modules, 2) a measure of the support for the existence of modular structures, and 3) an overall description of the whole structure, which may reveal hierarchical modular situations, in which modules are composed of smaller sub-modules.

| S-EPMC4892581 | biostudies-literature

SCOUT: simultaneous time segmentation and community detection in dynamic networks.

Project description:Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging.

| S-EPMC5121586 | biostudies-literature

DyCoNet: a Gephi plugin for community detection in dynamic complex networks.

Project description:Community structure detection has proven to be important in revealing the underlying organisation of complex networks. While most current analyses focus on static networks, the detection of communities in dynamic data is both challenging and timely. An analysis and visualisation procedure for dynamic networks is presented here, which identifies communities and sub-communities that persist across multiple network snapshots. An existing method for community detection in dynamic networks is adapted, extended, and implemented. We demonstrate the applicability of this method to detect communities in networks where individuals tend not to change their community affiliation very frequently. When stability of communities cannot be assumed, we show that the sub-community model may be a better alternative. This is illustrated through test cases of social and biological networks. A plugin for Gephi, an open-source software program used for graph visualisation and manipulation, named "DyCoNet", was created to execute the algorithm and is freely available from https://github.com/juliemkauffman/DyCoNet.

| S-EPMC4084810 | biostudies-literature

Robust dynamic community detection with applications to human brain functional networks.

Project description:While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications.

| S-EPMC7275079 | biostudies-literature

Game theory and extremal optimization for community detection in complex dynamic networks.

Project description:The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

| S-EPMC3935827 | biostudies-literature

Direct detection of circulating microRNA-122 using dynamic chemical labelling with single molecule detection overcomes stability and isomiR challenges for biomarker qualification

Project description:Circulating microRNAs are biomarkers reported to be stable and translational acrossspecies. miR-122 (miR-122-5p) is a hepatocyte-specific microRNA biomarker for drug-induced liver injury (DILI). Our objective was to develop an extraction-free andamplification-free detection method for measuring miR-122 that has translationalutility in context of DILI. We developed a single molecule dynamic chemical labelling(DCL) assay based on miR-122 hybridization to an abasic peptide nucleic acid probethat contained a reactive amine instead of a nucleotide at a specific position in thesequence. The single molecule DCL assay specifically measured miR-122 directly from10 μL of serum or plasma without any extraction steps, with a fit-for-purpose limit ofdetection of 1.32 pM. In 192 human serum samples, DCL accurately identified patientsat risk of DILI (area under ROC curve 0.98 (95%CI 0.96-1), P<0.0001). The miR-122assay also quantified liver injury in rats and dogs. When DCL beads were added toserum, the miR-122 signal was stabilised (no loss of signal after 14 days at roomtemperature). By contrast, there was substantial degradation of miR-122 in the absenceof beads (≈60% lost in 1 day). RNA sequencing demonstrated the presence of multiplemiR-122 isomiRs with DILI that were at low concentration or not present in healthypatient serum. Sample degradation over time produced more isomiRs, particularlyrapidly with DILI. PCR was inaccurate when analysing miR-122 isomiRs, whereas theDCL assay demonstrated accurate quantification. In summary, the DCL assay canaccurately measure miR-122 directly from serum and plasma to diagnose liver injury inhumans and other species, and can overcome important microRNA biomarker analyticaland biological challenges.

2019-11-23 | GSE140833 | GEO

In Situ Detection of Endogenous HIV Activation by Dynamic Nuclear Polarization NMR and Flow Cytometry.

Project description:We demonstrate for the first time in-cell dynamic nuclear polarization (DNP) in conjunction with flow cytometry sorting to address the cellular heterogeneity of in-cell samples. Utilizing a green fluorescent protein (GFP) reporter of HIV reactivation, we correlate increased 15N resonance intensity with cytokine-driven HIV reactivation in a human cell line model of HIV latency. As few as 10% GFP+ cells could be detected by DNP nuclear magnetic resonance (NMR). The inclusion of flow cytometric sorting of GFP+ cells prior to analysis by DNP-NMR further boosted signal detection through increased cellular homogeneity with respect to GFP expression. As few as 3.6 million 15N-labeled GFP+ cells could be readily detected with DNP-NMR. Importantly, cell sorting allowed for the comparison of cytokine-treated GFP+ and GFP- cells in a batch-consistent way. This provides an avenue for normalizing NMR spectral contributions from background cellular processes following treatment with cellular modulators. We also demonstrate the remarkable stability of AMUPol (a nitroxide biradical) in Jurkat T cells and achieved in-cell enhancements of 46 with 10 mM AMUPol, providing an excellent model system for further in-cell DNP-NMR studies. This represents an important contribution to improving in-cell methods for the study of endogenously expressed proteins by DNP-NMR.

| S-EPMC7369949 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data