Clustering Time Series by Network Community Analysis

2010 ◽  
Author(s):  
Carlo Piccardi ◽  
Lisa Calatroni
Keyword(s):  
Time Series ◽  
Community Analysis ◽  
Network Community

CLUSTERING FINANCIAL TIME SERIES BY NETWORK COMMUNITY ANALYSIS

2011 ◽  
Vol 22 (01) ◽  
pp. 35-50 ◽  
Author(s):  
CARLO PICCARDI ◽  
LISA CALATRONI ◽  
FABIO BERTONI
Keyword(s):  
Time Series ◽  
Stock Exchange ◽  
Financial Time Series ◽  
Community Analysis ◽  
Network Graph ◽  
Financial Time ◽  
Network Communities ◽  
The Us ◽  
The Stability ◽  
Symbolic Time Series Analysis

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

scholarly journals Landscape-Based Mutational Sensitivity Cartography and Network Community Analysis of the SARS-CoV-2 Spike Protein Structures: Quantifying Functional Effects of the Circulating Variants

2021 ◽  
Author(s):  
Gennady Verkhivker ◽  
Steve Agajanian ◽  
Deniz Yazar Oztas ◽  
Grace Gupta
Keyword(s):  
Molecular Mechanisms ◽  
Conformational Dynamics ◽  
Protein Structures ◽  
Community Analysis ◽  
Network Community ◽  
Residue Interaction ◽  
Domain Interactions ◽  
The Stability ◽  
Induced Changes ◽  
Spike Proteins

Structural and biochemical studies SARS-CoV-2 spike mutants with the enhanced infectivity have attracted significant attention and offered several mechanisms to explain the experimental data. In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined atomistic simulations, deep mutational scanning and sensitivity mapping together with the network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Conformational dynamics and analysis of collective motions in the SARS-CoV-2 spike proteins demonstrated that the D614 position anchors a key regulatory cluster that dictates functional transitions between open and closed states. Using mutational scanning and sensitivity analysis of the spike residues, we identified the evolution of stability hotspots in the SARS-CoV-2 spike structures of the mutant trimers. The results offer support to the reduced shedding mechanism of as a driver of the increased infectivity triggered by the D614G mutation. By employing the landscape-based network community analysis of the SARS-CoV-2 spike proteins, our results revealed that the D614G mutation can promote the increased number of stable communities in the open form by enhancing the stability of the inter-domain interactions. This study provides atomistic view of the interactions and stability hotspots in the SARS-CoV-2 spike proteins, offering a useful insight into the molecular mechanisms of the D614G mutation that can exert its functional effects through allosterically induced changes on stability of the residue interaction networks.

scholarly journals Changes in the analysis of temporal community dynamics data: a 29-year literature review

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11250 ◽  
Author(s):  
Hannah L. Buckley ◽  
Nicola J. Day ◽  
Gavin Lear ◽  
Bradley S. Case
Keyword(s):  
Time Series ◽  
Experimental Design ◽  
Best Practice ◽  
Community Dynamics ◽  
Temporal Dynamics ◽  
Habitat Type ◽  
Community Analysis ◽  
Median Number ◽  
Biological Communities ◽  
Short Time

Background Understanding how biological communities change over time is of increasing importance as Earth moves into the Anthropocene. A wide variety of methods are used for multivariate community analysis and are variously applied to research that aims to characterise temporal dynamics in community composition. Understanding these methods and how they are applied is useful for determining best practice in community ecology. Methodology We reviewed the ecological literature from 1990 to 2018 that used multivariate methods to address questions of temporal community dynamics. For each paper that fulfilled our search criteria, we recorded the types of multivariate analysis used to characterise temporal community dynamics in addition to the research aim, habitat type, location, taxon and the experimental design. Results Most studies had relatively few temporal replicates; the median number was seven time points. Nearly 70% of studies applied more than one analysis method; descriptive methods such as bar graphs and ordination were the most commonly applied methods. Surprisingly, the types of analyses used were only related to the number of temporal replicates, but not to research aim or any other aspects of experimental design such as taxon, or habitat or year of study. Conclusions This review reveals that most studies interested in understanding community dynamics use relatively short time series meaning that several, more sophisticated, temporal analyses are not widely applicable. However, newer methods using multivariate dissimilarities are growing in popularity and many can be applied to time series of any length.

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