A novel framework for community modeling and characterization in directed temporal networks

Abstract We deal with the problem of modeling and characterizing the community structure of complex systems. First, we propose a mathematical model for directed temporal networks based on the paradigm of activity driven networks. Many features of real-world systems are encapsulated in our model, such as hierarchical and overlapping community structures, heterogeneous attitude of nodes in behaving as sources or drains for connections, and the existence of a backbone of links that model dyadic relationships between nodes. Second, we develop a method for parameter identification of temporal networks based on the analysis of the integrated network of connections. Starting from any existing community detection algorithm, our method enriches the obtained solution by providing an in-depth characterization of the very nature of the role of nodes and communities in generating the temporal link structure. The proposed modeling and characterization framework is validated on three synthetic benchmarks and two real-world case studies

Modeling memory effects in activity-driven networks

Activity-driven networks (ADNs) have recently emerged as a powerful paradigm to study the temporal evolution of stochastic networked systems. All the information on the time-varying nature of the system is encapsulated into a constant activity parameter, which represents the propensity to generate connections. This formulation has enabled the scientific community to perform effective analytical studies on temporal networks. However, the hypothesis that the whole dynamics of the system is summarized by constant parameters might be excessively restrictive. Empirical studies suggest that activity evolves in time, intertwined with the system evolution, causing burstiness and clustering phenomena. In this paper, we propose a novel model for temporal networks, in which a self-excitement mechanism governs the temporal evolution of the activity, linking it to the evolution of the networked system. We investigate the effect of self-excitement on the epidemic inception by comparing the epidemic threshold of a Susceptible-Infected-Susceptible model in the presence and in the absence of the self-excitement mechanism. Our results suggest that the temporal nature of the activity favors the epidemic inception. Hence, neglecting self-excitement mechanisms might lead to harmful underestimation of the risk of an epidemic outbreak. Extensive numerical simulations are presented to support and extend our analysis, exploring parameter heterogeneities and noise, transient dynamics, and immunization processes. Our results constitute a first, necessary step toward a theory of ADNs that accounts for memory effects in the network evolution

Pattern Recognition for Complex Heterogeneous Time-Series Data: An Analysis of Microbial Community Dynamics

Microbial life is the most wide-spread and the most abundant life form on earth. They exist in complex and diverse communities in environments from the deep ocean trenches to Himalayan snowfields. Microbial life is essential for other forms of life as well. Scientific studies of microbial activity include diverse communities such as plant root microbiome, insect gut microbiome and human skin microbiome. In the human body alone, the number of microbial life forms supersedes the number of human body cells. Hence it is essential to understand microbial community dynamics. With the advent of 16S rRNA sequencing, we have access to a plethora of data on the microbiome, warrantying a shift from in-vitro analysis to in-silico analysis. This thesis focuses on challenges in analysing microbial community dynamics through complex, heterogeneous and temporal data. Firstly, we look at the mathematical modelling of microbial community dynamics and inference of microbial interaction networks by analysing longitudinal sequencing data. We look at the problem with the aims of minimising the assumptions involved and improving the accuracy of the inferred interaction networks. Secondly, we explore the temporally dynamic nature of microbial interaction networks. We look at the fallacies of static microbial interaction networks and approaches suitable for modelling temporally dynamic microbial interaction networks. Thirdly, we study multiple temporal microbial datasets from similar environments to understand macro and micro patterns apparent in these communities. We explore the individuality and conformity of microbial communities through visualisation techniques. Finally, we explore the possibility and challenges in representing heterogeneous microbial temporal activity in unique signatures. In summary, in this work, we have explored various aspects of complex, heterogeneous and time-series data through microbial temporal abundance datasets and have enhanced the knowledge about these complex and diverse communities through a pattern recognition approach

Advanced techniques for visual analysis of temporal networks

Temporal networks represent interactions among entities of a given domain with additional information about when such interactions occur. The visualization of temporal networks plays a key role in the recognition of properties that would be difficult to perceive without an adequate visualization strategy. Due to a large amount of information provided by these networks, more attention has been given to issues related to the visual scalability associated with the produced layouts, but this still represents an unsolved problem and lacks effective techniques. We propose in this thesis novel techniques to enhance the visualization of temporal networks. Specifically, a scalable node reordering technique for temporal network visualization, named Community-based Node Ordering (CNO), combining static community detection with node reordering techniques, along with a taxonomy to categorize activity patterns. In addition, a visualization method that allows the comparison of two community detection algorithms is presented in order to decide which one is better for visual analysis of communities. Another contribution is the analysis of dynamic processes, as spreading rumors, diseases, applied in the visualization of temporal networks. Furthermore, we conducted a user experiment consisting of the application of different tasks in temporal networks, in order to find the relation of the layouts with the most appropriate tasks. Finally, the Dynamic Network Visualization (DyNetVis) system demonstrates the software specifications, examples, functionalities, and impact in the study field. We performed experiments with qualitative and quantitative analyses using real networks in several fields to show that the proposed layouts and categorization helped in the identification of patterns that would otherwise be difficult to see.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorTese (Doutorado)Redes temporais representam interações entre entidades do domínio com a informação adicional de quando essas comunicações ocorrem. A visualização de redes temporais tem um importante papel no reconhecimento de propriedades das redes que seriam difíceis de serem percebidas sem uma estratégia de visualização adequada. Devido à grande quantidade de informação nessas redes, mais atenção tem sido dada em relação a escalabilidade visual associada com visualizações produzidas, mas ainda representa um problema não resolvido e com falta de abordagens específicas. Neste trabalho são propostas novas estratégias para melhorar a visualização de redes temporais. Especificamente, é proposta uma técnica de ordenação de nós escalável para a visualização de redes temporais, chamada de Community-based Node Ordering (CNO), que combina detecção de comunidade estática com técnicas de ordenação de nós, juntamente com uma taxonomia para categorizar os padrões das atividades. É apresentado também um método de visualização que permite a comparação entre dois algoritmos de detecção de comunidades para ajudar a decidir qual deles é melhor para a análise visual de comunidades. Também são abordados estratégias para a visualização de processos dinâmicos em redes, como espalhamento de rumores e doenças. Além disso, é conduzido um experimento com usuário com a definição de diferentes tarefas em redes temporais, a fim de identificar quais são as melhores formas de visualizar de acordo com diferentes tarefas. Por fim, é descrito o sistema Dynamic Network Visualization (DyNetVis), mostrado suas especificações, requisitos, funcionalidades e impacto na área. Os experimentos foram gerados com análises quantitativas e qualitativas utilizando redes reais em diferentes contextos, para mostrar que as visualizações propostas e suas categorizações ajudaram na identificação de padrões que seriam difíceis de serem vistos sem o uso dessas técnicas de visualização

A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions

MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis.

Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses

The application of network analysis to assess the structure and function of aquatic food webs : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Ecology at Massey University, Manawatu, New Zealand

The health of aquatic communities is under threat globally by anthropogenic impacts. A healthy ecological community is one that maintains its structure and function over time in the face of disturbance (i.e., they are stable). If we are to effectively monitor change in ecological health and instigate appropriate environmental management responses, then we first need to measure ecological health appropriately. Most methods of indicating ecological health in rivers measure structural aspects of a community, with little attention given to functional aspects. Ecological network analysis (ENA) provides a range of food web metrics that can measure both structural and functional aspects of ecological communities. The aim of this thesis was to apply ENA metrics to assess the structure and function of aquatic ecosystems and explore how they may change with habitat. In a general comparison of aquatic ecosystems, I found that rivers, lakes and estuaries have structurally similar food webs, except have lower neighbourhood connectivity which is reminiscent of unstable habitats. Through species extinction simulations of aquatic energy flow networks, I showed that aquatic food webs were most stable when trophic cascades were weak and average trophic levels were small. In examining the effects of riparian deforestation in Taranaki rivers, dietary changes altered the structure of riverine macroinvertebrate communities considerably and drove greater community respiration. In the Hutt River, I modelled changes in the biomass of trout (exotic predator) and periphyton, and showed that more periphyton, but not more trout, can result in greater community temporal variability. Furthermore, increased trout and periphyton can drive more interspecific competition. I also demonstrated the need for managers to consider the impacts of decisions on adjacent ecosystems as well as target ecosystem by showing that the Hutt River and Wellington Harbour respond substantially different to increases in algal biomass. Finally in rivers differing in nutrient enrichment the Manawatu, I showed that food webs in enriched rivers may be more stable to random species loss but more susceptible to species loss from floods. Similarly to riparian deforestation, highly enriched rivers had greater community respiration (excluding microbial activity), which may exacerbate hypoxic conditions and drive the loss of sensitive species