Network Analysis Of Time Series Research Articles

A View on technology and controls for epidemic preparedness and the growth of COVID-19 disease in Saudi Arabia

This work emphasizes that the best chance of ending the pandemic lies in the Measures that can help us respond to these epidemic threats more effectively than before in the scope of protection from epidemic risks that we were not aware of before the outbreak of the disease, which is to benefit from the study of previous experiences. The work show the importance of technology about how to prepare for and handle any pandemic by discussing a successful case from the past. We examine Saudi Arabia's COVID-19 pandemic response experiences. We proposed a case evolution study from the southern region of Jazan to demonstrate the consequences of these techniques. Data on the daily Jazan COVID-19 infection curve collected from July 1st  to September 30th, 2020, was processed using multiple Python software modules, such as encoder-decoder LSTM, 1D-CNN, and Complex network analysis of time series to identify changes within the infection curve's structure. A Gaussian modeling study was performed to compare the data movement with the protection from epidemic risks computed by stratification based on the data group.The network analysis shows that hubs are stable while medium and low-degree nodes are unstable. Also, from the perspective of Gaussian modeling, the COVID-19 infection curve parts indicate that the pandemic in Saudi Arabia is about to peak and move into the second (declining) half of the bell-shaped distribution.Technology in many areas and internet access make Saudi Arabia's social separation strategies a pandemic-eradication success story

An analysis and prediction model based on complex network time series

AbstractAt present, mainstream research on complex network evolution involves measuring the developing characteristics of network structure at the macro level. Few studies have focused on the internal mechanism of network structure change and the prediction of network structure evolution at the micro‐level. However, for some special complex network time series, the traditional analysis and research methods at the macro level are invalid, and the micro‐level analysis method needs to be adopted. Based on the evolution characteristics of nodes and edges in complex networks, this paper constructs a time series network analysis and prediction algorithm to explore the structural evolution trends of complex network time series and make a prediction. Based on the enterprise supply–demand relationship data, this paper constructs the enterprise supply–demand network time series. Then, we use the method proposed in this paper to analyse and predict the complex network time series and verify the rationality and feasibility of the proposed method.

Confidence, accuracy judgments and feedback in schizophrenia and bipolar disorder: a time series network analysis.

Inaccurate self-assessment of performance is common among people with serious mental illness, and it is associated with poor functional outcomes independent from ability. However, the temporal interdependencies between judgments of performance, confidence in accuracy, and feedback about performance are not well understood. We evaluated two tasks: the Wisconsin Card Sorting Test (WCST) and the Penn Emotion recognition task (ER40). These tasks were modified to include item-by-item confidence and accuracy judgments, along with feedback on accuracy. We evaluated these tasks as time series and applied network modeling to understand the temporal relationships between momentary confidence, accuracy judgments, and feedback. The sample constituted participants with schizophrenia (SZ; N = 144), bipolar disorder (BD; N = 140), and healthy controls (HC; N = 39). Network models for both WCST and ER40 revealed denser and lagged connections between confidence and accuracy judgments in SZ and, to a lesser extent in BD, that were not evidenced in HC. However, associations between feedback regarding accuracy with subsequent accuracy judgments and confidence were weaker in SZ and BD. In each of these comparisons, the BD group was intermediate between HC and SZ. In analyses of the WCST, wherein incorporating feedback is crucial for success, higher confidence predicted worse subsequent performance in SZ but not in HC or BD. While network models are exploratory, the results suggest some potential mechanisms by which challenges in self-assessment may impede performance, perhaps through hyperfocus on self-generated judgments at the expense of incorporation of feedback.

A multi-level, time-series network analysis of the impact of youth peacebuilding on quality peace

PurposeOver 60% of armed conflicts re-occur; the seed of future conflict is sown even as a peace agreement is signed. The cyclical nature of war calls for a focus on youth who can disrupt this pattern over time. Addressing this concern, the developmental peace-building model calls for a dynamic, multi-level and longitudinal approach. Using an innovative statistical approach, this study aims to investigate the associations among four youth peace-building dimensions and quality peace.Design/methodology/approachMulti-level time-series network analysis of a data set containing 193 countries and spanning the years between 2011 and 2020 was performed. This statistical approach allows for complex modelling that can reveal new patterns of how different youth peace-building dimensions (i.e. education, engagement, information, inclusion), identified through rapid evidence assessment, promote quality peace over time. Such a methodology not only assesses between-country differences but also within-country change.FindingsWhile the within-country contemporaneous network shows positive links for education, the temporal network shows significant lagged effects for all four dimensions on quality peace. The between-country network indicates significant direct effects of education and information, on average, and indirect effects of inclusion and engagement, on quality peace.Originality/valueThis approach demonstrates a novel application of multi-level time-series network analysis to explore the dynamic development of quality peace, capturing both stability and change. The analysis illustrates how youth peace-building dimensions impact quality peace in the macro-system globally. This investigation of quality peace thus illustrates that the science of peace does not necessitate violent conflict.

Network Analysis of Time Series: Novel Approaches to Network Neuroscience.

In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches.

Motivation in schizophrenia: preliminary findings of a theory-driven approach using time-series network analysis

Motivational negative symptoms are an important obstacle to the daily functioning of individuals with schizophrenia. A better understanding of associated processes would help to identify targets for interventions. We used Experience Sampling Method (ESM) to assess the daily cognitive, behavioural and emotional/hedonic processes identified in a redefined theoretical model of motivation in schizophrenia. Further, we applied multilevel network analysis to explore in a preliminary way the potential causal factors that may influence the daily course of motivation. Using a 14-day paradigm ESM in 7 participants with schizophrenia spectrum disorders (276 observations), we examined the interplay between the processes derived from the proposed model. We estimated both contemporaneous and temporal networks using multilevel vector autoregressive analyses for time-series. The contemporaneous network showed that motivation was central and strongly related to levels of energy and (self-)confidence. The temporal network showed that savouring processes (i.e. reminiscence and projection into the future) were the most influential and had an impact on later motivation, mood, effort, energy, (self-)confidence, and discouraging beliefs. These findings provide preliminary evidence in favour of the motivational model presented in this study, shedding new light on the understanding of motivation in schizophrenia and specifically identifying energy levels, savouring processes and confidence as likely beneficial targets for interventions.

Changes in the Research Trends of JKSC through Time Series Network Analysis

This paper aims to analyze research trends of the studies published in the Journal of the Korean Society of Costume (JKSC). Time series network analysis and word cloud analysis were employed to examine the articles published from 2008 to 2019. The findings revealed that fashion design and aesthetics research accounted the highest proportion of published articles as well as the highest citation impact, indicating that this field of study was the core research area and specificity of JKSC during the study period. The history of costume research showed a decreasing trend, while fashion marketing and psychology research showed an increase in the number of published articles. Word cloud analysis identified that keywords related to the history of costume research was another specificity of the journal. However, degree centrality value of the keywords related to the history of costume decreased in time series network analysis, indicating that the specificity faded. The importance of keywords related to fashion marketing and psychology has increased, revealing that the research interests shifted from fundamental studies to practical studies on the general apparel industry. The analysis is used as a basis to suggest future research directions to journals and researchers in clothing and textile studies.

Network analysis of swine movements in a multi-site pig production system in Iowa, USA

Pig production in the United States is based on multi-site systems in which pigs are transported between farms after the conclusion of each particular production phase. Although ground transportation is a critical component of the pork supply chain, it might constitute a potential route of infectious disease dissemination. Here, we used a time series network analysis to: (1) describe pig movement flow in a multi-site production system in Iowa, USA, (2) conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and (3) assess the potential impact of each farm type on disease dissemination across the system. Movement reports from 2014–2016 were provided by Iowa Select Farms, Iowa Fall, IA. A total of 76,566 shipments across sites was analyzed, and time series network analyses with temporal resolution of 1, 3, 6, 12, and 36 months were considered. The general topological properties of networks with resolution of 1, 3, 6, and 12 months were compared with the whole period static network (36 months) and included the following features: number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Small-world and scale-free topologies, centrality parameters, and percolation analysis were investigated for the networks with 1-month window. Networks’ robustness to interventions was assessed by using the Basic Reproduction Number (R0). Centrality parameters indicate that gilt development units (GDU), nursery, and sow farms have more central role in the pig production hierarchical structure. Therefore, they are potentially major factors of introduction and spread of diseases over the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to be infected. Percolation analysis combined with general properties (i.e. heavy-tailed distributions and degree disassortative) suggested that networks with 1-month time resolution were highly responsive to interventions. Furthermore, the characteristics of a disease should have strong implications in the biosecurity practices across production sites. For instance, biosecurity practices should be focused on sow farms for highly contagious disease (e.g., foot and mouth disease), while it should target nursery sites in the case of a less contagious diseases (i.e. mycobacterial infections). Understanding the patterns of swine movements is crucial for the swine industry decision-making in the case of an epidemic, as well as to design cost-effective approaches to monitor, prevent, control and eradicate infectious diseases in multi-site systems.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Complex network analysis of time series

Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.

시계열 네트워크분석을 통한 데이터품질 연구경향 및 산업연관 분석

본 연구는 데이터품질과 관련된 선행연구의 메타정보를 활용하여 연구경향을 분석하고 이를 통해서 산업계의 흐름을 예측하기 위한 목적의 연구이다. 다양한 분야에서 연구경향을 분석하려는 시도는 이어져 왔으나, 데이터품질 영역은 그 범위가 방대하여 선행 연구자료에 대한 분석을 수행하기 어려웠다. 본 연구는 Web of Science 색인DB에 수록된 최근 10년간의 연구 메타데이터를 수집하여 텍스트 마이닝, 사회연결망 분석기법을 활용한 시계열 네트워크 분석을 수행하였다. 연구주제 분석 결과, 수학 및 전산 생물학, 화학, 건강관리 과학 및 서비스, 생화학 및 분자 생물학, 운영 연구 및 경영 과학, 의료정보학은 연구비율이 감소하고 있었고, 환경, 수자원, 지질학, 계측기 및 계측의 연구비율은 증가하고 있었다. 또한 사회연결망 분석 결과 데이터품질 연구에서는 분석, 알고리즘, 네트워크의 주제가 중앙성이 높은 중요한 주제로 나타났으며, 이미지와 모델, 센서, 최적화가 데이터품질에서 중요한 주제로 등장하는 추세를 보였다. 데이터품질의 산업과 연관관계 분석 결과는 기술, 산업, 건강, 유틸리티, 고객서비스가 연관성이 높은 산업으로 나타났다. 본 연구의 결과는 데이터품질 연구의 패턴을 분석하고 산업과 연관관계를 찾는 데이터품질 관련 연구자 뿐아니라 산업계에도 유용한 자료로 활용되리라 판단된다. The purpose of this paper is both to analyze research trends and to predict industrial flows using the meta-data from the previous studies on data quality. There have been many attempts to analyze the research trends in various fields till lately. However, analysis of previous studies on data quality has produced poor results because of its vast scope and data. Therefore, in this paper, we used a text mining, social network analysis for time series network analysis to analyze the vast scope and data of data quality collected from a Web of Science index database of papers published in the international data quality-field journals for 10 years. The analysis results are as follows: Decreases in Mathematical & Computational Biology, Chemistry, Health Care Sciences & Services, Biochemistry & Molecular Biology, Biochemistry & Molecular Biology, and Medical Information Science. Increases, on the contrary, in Environmental Sciences, Water Resources, Geology, and Instruments & Instrumentation. In addition, the social network analysis results show that the subjects which have the high centrality are analysis, algorithm, and network, and also, image, model, sensor, and optimization are increasing subjects in the data quality field. Furthermore, the industrial connection analysis result on data quality shows that there is high correlation between technique, industry, health, infrastructure, and customer service. And it predicted that the Environmental Sciences, Biotechnology, and Health Industry will be continuously developed. This paper will be useful for people, not only who are in the data quality industry field, but also the researchers who analyze research patterns and find out the industry connection on data quality.

Network analysis of time series under the constraint of fixed nearest neighbors

In this paper, we carried out network analysis for typical time series, such as periodic signals, chaotic maps, Gaussian white noise, and fractal Brownian motions. By reconstructing the phase space for a given time series, we can generate a network under the constraint of fixed nearest neighbors. The mapped networks are then analyzed from both the statistical properties, such as degree distribution, clustering coefficient, betweenness, etc, as well as the local topological structures, i.e., network motifs. It is shown that time series of different nature can be distinguished from these two aspects of the constructed networks.

Analytical framework for recurrence network analysis of time series

Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclimatology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an ɛ-recurrence network as a discrete subnetwork of a "continuous" graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ɛ. In particular, we introduce local measures such as the ɛ-clustering coefficient, mesoscopic measures such as ɛ-motif density, path-based measures such as ɛ-betweennesses, and global measures such as ɛ-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ɛ via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasiperiodic motions, and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments. More generally, the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints, which appear frequently in disciplines ranging from biology to climate science.