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  1. Book ; Online: From Time Series to Networks in R with the ts2net Package

    Ferreira, Leonardo N.

    2022  

    Abstract: Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or ... ...

    Abstract Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. R is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents ts2net, an R package for modeling one or multiple time series into networks. The package provides the time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. Ts2net also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, ts2net permits using network science and graph mining tools to extract information from time series.
    Keywords Computer Science - Social and Information Networks ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-08-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: The small-world network of global protests.

    Ferreira, Leonardo N / Hong, Inho / Rutherford, Alex / Cebrian, Manuel

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 19215

    Abstract: Protest diffusion is a cascade process that can spread over different regions of the planet. The way and the extension that this phenomenon can occur is still not properly understood. Here, we empirically investigate this question using protest data from ...

    Abstract Protest diffusion is a cascade process that can spread over different regions of the planet. The way and the extension that this phenomenon can occur is still not properly understood. Here, we empirically investigate this question using protest data from GDELT and ICEWS, two of the most extensive and longest-running data sets freely available. We divide the globe into grid cells and construct a temporal network for each data set where nodes represent cells and links are established between nodes if their protest events co-occur. We show that the temporal networks are small-world, indicating that the cells are directly linked or separated by a few steps on average. Furthermore, the average path lengths are decreasing through the years, which suggests that the world is becoming "smaller". The persistent temporal hubs present in both data sets indicate that protests can spread faster through the hubs. This topological feature is consistent with the hypothesis that protests can quickly diffuse from one region to any other part of the globe.
    Language English
    Publishing date 2021-09-28
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-98628-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Global fire season severity analysis and forecasting

    Ferreira, Leonardo N / Vega-Oliveros, Didier A / Zhao, Liang / Cardoso, Manoel F / Macau, Elbert E.N

    Computers & geosciences. 2019 Oct. 22,

    2019  

    Abstract: Fire activity has a huge impact on human lives. Different models have been proposed to predict fire activity, which can be classified into global and regional ones. Global fire models focus on longer timescale simulations and can be very complex. ... ...

    Abstract Fire activity has a huge impact on human lives. Different models have been proposed to predict fire activity, which can be classified into global and regional ones. Global fire models focus on longer timescale simulations and can be very complex. Regional fire models concentrate on seasonal forecasting but usually require inputs that are not available in many places. Motivated by the possibility of having a simple, fast, and general model, we propose a seasonal fire prediction methodology based on time series forecasting methods. It consists of dividing the studied area into grid cells and extracting time series of fire counts to fit the forecasting models. We apply these models to estimate the fire season severity (FSS) from each cell, here defined as the sum of the fire counts detected in a season. Experimental results using a global fire detection data set show that the proposed approach can predict FSS with a relatively low error in many regions. The proposed approach is reasonably fast and can be applied on a global scale.
    Keywords data collection ; fire detection ; fire season ; models ; prediction ; time series analysis
    Language English
    Dates of publication 2019-1022
    Publishing place Elsevier Ltd
    Document type Article
    Note Pre-press version
    ISSN 0098-3004
    DOI 10.1016/j.cageo.2019.104339
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Spatiotemporal data analysis with chronological networks.

    Ferreira, Leonardo N / Vega-Oliveros, Didier A / Cotacallapa, Moshé / Cardoso, Manoel F / Quiles, Marcos G / Zhao, Liang / Macau, Elbert E N

    Nature communications

    2020  Volume 11, Issue 1, Page(s) 4036

    Abstract: The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. ...

    Abstract The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets.
    Language English
    Publishing date 2020-08-12
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-020-17634-2
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Spatiotemporal data analysis with chronological networks

    Ferreira, Leonardo N. / Vega-Oliveros, Didier A. / Cotacallapa, Moshe / Cardoso, Manoel F. / Quiles, Marcos G. / Zhao, Liang / Macau, Elbert E. N.

    2020  

    Abstract: The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a ... ...

    Abstract The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.
    Keywords Computer Science - Social and Information Networks ; Computer Science - Machine Learning ; Physics - Physics and Society ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-04-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Dynamic Community Detection into Analyzing of Wildfires Events

    Marli, Alessandra / Vega-Oliveros, Didier A / Cotacallapa, Moshé / Ferreira, Leonardo N / Macau, Elbert EN / Quiles, Marcos G

    2020  

    Abstract: The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include ... ...

    Abstract The study and comprehension of complex systems are crucial intellectual and scientific challenges of the 21st century. In this scenario, network science has emerged as a mathematical tool to support the study of such systems. Examples include environmental processes such as wildfires, which are known for their considerable impact on human life. However, there is a considerable lack of studies of wildfire from a network science perspective. Here, employing the chronological network concept -- a temporal network where nodes are linked if two consecutive events occur between them -- we investigate the information that dynamic community structures reveal about the wildfires' dynamics. Particularly, we explore a two-phase dynamic community detection approach, i.e., we applied the Louvain algorithm on a series of snapshots. Then we used the Jaccard similarity coefficient to match communities across adjacent snapshots. Experiments with the MODIS dataset of fire events in the Amazon basing were conducted. Our results show that the dynamic communities can reveal wildfire patterns observed throughout the year.

    Comment: 16 pages, 8 figures
    Keywords Computer Science - Social and Information Networks ; Computer Science - Machine Learning ; Physics - Data Analysis ; Statistics and Probability ; Physics - Physics and Society
    Subject code 006
    Publishing date 2020-11-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Long-range correlations and fractal dynamics in C. elegans: Changes with aging and stress.

    Alves, Luiz G A / Winter, Peter B / Ferreira, Leonardo N / Brielmann, Renée M / Morimoto, Richard I / Amaral, Luís A N

    Physical review. E

    2017  Volume 96, Issue 2-1, Page(s) 22417

    Abstract: Reduced motor control is one of the most frequent features associated with aging and disease. Nonlinear and fractal analyses have proved to be useful in investigating human physiological alterations with age and disease. Similar findings have not been ... ...

    Abstract Reduced motor control is one of the most frequent features associated with aging and disease. Nonlinear and fractal analyses have proved to be useful in investigating human physiological alterations with age and disease. Similar findings have not been established for any of the model organisms typically studied by biologists, though. If the physiology of a simpler model organism displays the same characteristics, this fact would open a new research window on the control mechanisms that organisms use to regulate physiological processes during aging and stress. Here, we use a recently introduced animal-tracking technology to simultaneously follow tens of Caenorhabdits elegans for several hours and use tools from fractal physiology to quantitatively evaluate the effects of aging and temperature stress on nematode motility. Similar to human physiological signals, scaling analysis reveals long-range correlations in numerous motility variables, fractal properties in behavioral shifts, and fluctuation dynamics over a wide range of timescales. These properties change as a result of a superposition of age and stress-related adaptive mechanisms that regulate motility.
    MeSH term(s) Aging/physiology ; Animals ; Caenorhabditis elegans/physiology ; Fractals ; Image Processing, Computer-Assisted ; Models, Biological ; Movement/physiology ; Stress, Physiological/physiology ; Temperature ; Video Recording
    Language English
    Publishing date 2017-08-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2844562-4
    ISSN 2470-0053 ; 2470-0045
    ISSN (online) 2470-0053
    ISSN 2470-0045
    DOI 10.1103/PhysRevE.96.022417
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Measuring the engagement level in encrypted group conversations by using temporal networks

    Cotacallapa, Moshe / Berton, Lilian / Ferreira, Leonardo N. / Quiles, Marcos G. / Zhao, Liang / Macau, Elbert E. N. / Vega-Oliveros, Didier A.

    2019  

    Abstract: Chat groups are well-known for their capacity to promote viral political and marketing campaigns, spread fake news, and create rallies by hundreds of thousands on the streets. Also, with the increasing public awareness regarding privacy and surveillance, ...

    Abstract Chat groups are well-known for their capacity to promote viral political and marketing campaigns, spread fake news, and create rallies by hundreds of thousands on the streets. Also, with the increasing public awareness regarding privacy and surveillance, many platforms have started to deploy end-to-end encrypted protocols. In this context, the group's conversations are not accessible in plain text or readable format by third-party organizations or even the platform owner. Then, the main challenge that emerges is related to getting insights from users' activity of those groups, but without accessing the messages. Previous approaches evaluated the user engagement by assessing user's activity, however, on limited conditions where the data is encrypted, they cannot be applied. In this work, we present a framework for measuring the level of engagement of group conversations and users, without reading the messages. Our framework creates an ensemble of interaction networks that represent the temporal evolution of the conversation, then, we apply the proposed Engagement Index (EI) for each interval of conversations to assess users' participation. Our results in five datasets from real-world WhatsApp Groups indicate that, based on the EI, it is possible to identify the most engaged users within a time interval, create rankings, and group users according to their engagement and monitor their performance over time.

    Comment: 8 pages, 9 figures, IJCNN
    Keywords Computer Science - Social and Information Networks
    Subject code 360
    Publishing date 2019-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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