Assessing biases in phylodynamic inferences in the presence of super-spreaders.

Phylodynamic analyses using pathogen genetic data have become popular for making epidemiological inferences. However, many methods assume that the underlying host population follows homogenous mixing patterns. Nevertheless, in real disease outbreaks, a small number of individuals infect a disproportionately large number of others (super-spreaders). Our objective was to quantify the degree of bias in estimating the epidemic starting date in the presence of super-spreaders using different sample selection strategies. We simulated 100 epidemics of a hypothetical pathogen (fast evolving foot and mouth disease virus-like) over a real livestock movement network allowing the genetic mutations in pathogen sequence. Genetic sequences were sampled serially over the epidemic, which were then used to estimate the epidemic starting date using Extended Bayesian Coalescent Skyline plot (EBSP) and Birth-death skyline plot (BDSKY) models. Our results showed that the degree of bias varies over different epidemic situations, with substantial overestimations on the epidemic duration occurring in some occasions. While the accuracy and precision of BDSKY were deteriorated when a super-spreader generated a larger proportion of secondary cases, those of EBSP were deteriorated when epidemics were shorter. The accuracies of the inference were similar irrespective of whether the analysis used all sampled sequences or only a subset of them, although the former required substantially longer computational times. When phylodynamic analyses need to be performed under a time constraint to inform policy makers, we suggest multiple phylodynamics models to be used simultaneously for a subset of data to ascertain the robustness of inferences

Dynamic Core Community Detection and Information Diffusion Processes on Networks

Interest in network science has been increasingly shared among various research communities due to its broad range of applications. Many real world systems can be abstracted as networks, a group of nodes connected by pairwise edges, and examples include friendship networks, metabolic networks, and world wide web among others. Two of the main research areas in network science that have received a lot of focus are community detection and information diffusion. As for community detection, many well developed algorithms are available for such purposes in static networks, for example, spectral partitioning and modularity function based optimization algorithms. As real world data becomes richer, community detection in temporal networks becomes more and more desirable and algorithms such as tensor decomposition and generalized modularity function optimization are developed. One scenario not well investigated is when the core community structure persists over long periods of time with possible noisy perturbations and changes only over periods of small time intervals. The contribution of this thesis in this area is to propose a new algorithm based on low rank component recovery of adjacency matrices so as to identify the phase transition time points and improve the accuracy of core community structure recovery. As for information diffusion, traditionally it was studied using either threshold models or independent interaction models as an epidemic process. But information diffusion mechanism is different from epidemic process such as disease transmission because of the reluctance to tell stale news and to address this issue other models such as DK model was proposed taking into consideration of the reluctance of spreaders to diffuse the information as time goes by. However, this does not capture some cases such as the losing interest of information receivers as in viral marketing. The contribution of this thesis in this area is we proposed two new models coined susceptible-informed-immunized (SIM) model and exponentially time decaying susceptible-informed (SIT) model to successfully capture the intrinsic time value of information from both the spreader and receiver points of view. Rigorous analysis of the dynamics of the two models were performed based mainly on mean field theory. The third contribution of this thesis is on the information diffusion optimization. Controlling information diffusion has been widely studied because of its important applications in areas such as social census, disease control and marketing. Traditionally the problem is formulated as identifying the set of k seed nodes, informed initially, so as to maximize the diffusion size. Heuristic algorithms have been developed to find approximate solutions for this NP-hard problem, and measures such as k-shell, node degree and centrality have been used to facilitate the searching for optimal solutions. The contribution of this thesis in this field is to design a more realistic objective function and apply binary particle swarm optimization algorithm for this combinatorial optimization problem. Instead of fixating the seed nodes size and maximize the diffusion size, we maximize the profit defined as the revenue, which is simply the diffusion size, minus the cost of setting those seed nodes, which is designed as a function of degrees of the seed nodes or a measure that is similar to the centrality of nodes. Because of the powerful algorithm, we were able to study complex scenarios such as information diffusion optimization on multilayer networks.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145937/1/wbao_1.pd

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table

Epidemic processes in complex networks

In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

Identification of key players in networks using multi-objective optimization and its applications

Identification of a set of key players, is of interest in many disciplines such as sociology, politics, finance, economics, etc. Although many algorithms have been proposed to identify a set of key players, each emphasizes a single objective of interest. Consequently, the prevailing deficiency of each of these methods is that, they perform well only when we consider their objective of interest as the only characteristic that the set of key players should have. But in complicated real life applications, we need a set of key players which can perform well with respect to multiple objectives of interest. In this dissertation, a new perspective for key player identification is proposed, based on optimizing multiple objectives of interest. The proposed approach is useful in identifying both key nodes and key edges in networks. Experimental results show that the sets of key players which optimize multiple objectives perform better than the key players identified using existing algorithms, in multiple applications such as eventual influence limitation problem, immunization problem, improving the fault tolerance of the smart grid, etc. We utilize multi-objective optimization algorithms to optimize a set of objectives for a particular application. A large number of solutions are obtained when the number of objectives is high and the objectives are uncorrelated. But decision-makers usually require one or two solutions for their applications. In addition, the computational time required for multi-objective optimization increases with the number of objectives. A novel approach to obtain a subset of the Pareto optimal solutions is proposed and shown to alleviate the aforementioned problems. As the size and the complexity of the networks increase, so does the computational effort needed to compute the network analysis measures. We show that degree centrality based network sampling can be used to reduce the running times without compromising the quality of key nodes obtained

On the Detection of False Information: From Rumors to Fake News

Tesis por compendio[ES] En tiempos recientes, el desarrollo de las redes sociales y de las agencias de noticias han traído nuevos retos y amenazas a la web. Estas amenazas han llamado la atención de la comunidad investigadora en Procesamiento del Lenguaje Natural (PLN) ya que están contaminando las plataformas de redes sociales. Un ejemplo de amenaza serían las noticias falsas, en las que los usuarios difunden y comparten información falsa, inexacta o engañosa. La información falsa no se limita a la información verificable, sino que también incluye información que se utiliza con fines nocivos. Además, uno de los desafíos a los que se enfrentan los investigadores es la gran cantidad de usuarios en las plataformas de redes sociales, donde detectar a los difusores de información falsa no es tarea fácil. Los trabajos previos que se han propuesto para limitar o estudiar el tema de la detección de información falsa se han centrado en comprender el lenguaje de la información falsa desde una perspectiva lingüística. En el caso de información verificable, estos enfoques se han propuesto en un entorno monolingüe. Además, apenas se ha investigado la detección de las fuentes o los difusores de información falsa en las redes sociales. En esta tesis estudiamos la información falsa desde varias perspectivas. En primer lugar, dado que los trabajos anteriores se centraron en el estudio de la información falsa en un entorno monolingüe, en esta tesis estudiamos la información falsa en un entorno multilingüe. Proponemos diferentes enfoques multilingües y los comparamos con un conjunto de baselines monolingües. Además, proporcionamos estudios sistemáticos para los resultados de la evaluación de nuestros enfoques para una mejor comprensión. En segundo lugar, hemos notado que el papel de la información afectiva no se ha investigado en profundidad. Por lo tanto, la segunda parte de nuestro trabajo de investigación estudia el papel de la información afectiva en la información falsa y muestra cómo los autores de contenido falso la emplean para manipular al lector. Aquí, investigamos varios tipos de información falsa para comprender la correlación entre la información afectiva y cada tipo (Propaganda, Trucos / Engaños, Clickbait y Sátira). Por último, aunque no menos importante, en un intento de limitar su propagación, también abordamos el problema de los difusores de información falsa en las redes sociales. En esta dirección de la investigación, nos enfocamos en explotar varias características basadas en texto extraídas de los mensajes de perfiles en línea de tales difusores. Estudiamos diferentes conjuntos de características que pueden tener el potencial de ayudar a discriminar entre difusores de información falsa y verificadores de hechos.[CA] En temps recents, el desenvolupament de les xarxes socials i de les agències de notícies han portat nous reptes i amenaces a la web. Aquestes amenaces han cridat l'atenció de la comunitat investigadora en Processament de Llenguatge Natural (PLN) ja que estan contaminant les plataformes de xarxes socials. Un exemple d'amenaça serien les notícies falses, en què els usuaris difonen i comparteixen informació falsa, inexacta o enganyosa. La informació falsa no es limita a la informació verificable, sinó que també inclou informació que s'utilitza amb fins nocius. A més, un dels desafiaments als quals s'enfronten els investigadors és la gran quantitat d'usuaris en les plataformes de xarxes socials, on detectar els difusors d'informació falsa no és tasca fàcil. Els treballs previs que s'han proposat per limitar o estudiar el tema de la detecció d'informació falsa s'han centrat en comprendre el llenguatge de la informació falsa des d'una perspectiva lingüística. En el cas d'informació verificable, aquests enfocaments s'han proposat en un entorn monolingüe. A més, gairebé no s'ha investigat la detecció de les fonts o els difusors d'informació falsa a les xarxes socials. En aquesta tesi estudiem la informació falsa des de diverses perspectives. En primer lloc, atès que els treballs anteriors es van centrar en l'estudi de la informació falsa en un entorn monolingüe, en aquesta tesi estudiem la informació falsa en un entorn multilingüe. Proposem diferents enfocaments multilingües i els comparem amb un conjunt de baselines monolingües. A més, proporcionem estudis sistemàtics per als resultats de l'avaluació dels nostres enfocaments per a una millor comprensió. En segon lloc, hem notat que el paper de la informació afectiva no s'ha investigat en profunditat. Per tant, la segona part del nostre treball de recerca estudia el paper de la informació afectiva en la informació falsa i mostra com els autors de contingut fals l'empren per manipular el lector. Aquí, investiguem diversos tipus d'informació falsa per comprendre la correlació entre la informació afectiva i cada tipus (Propaganda, Trucs / Enganys, Clickbait i Sàtira). Finalment, però no menys important, en un intent de limitar la seva propagació, també abordem el problema dels difusors d'informació falsa a les xarxes socials. En aquesta direcció de la investigació, ens enfoquem en explotar diverses característiques basades en text extretes dels missatges de perfils en línia de tals difusors. Estudiem diferents conjunts de característiques que poden tenir el potencial d'ajudar a discriminar entre difusors d'informació falsa i verificadors de fets.[EN] In the recent years, the development of social media and online news agencies has brought several challenges and threats to the Web. These threats have taken the attention of the Natural Language Processing (NLP) research community as they are polluting the online social media platforms. One of the examples of these threats is false information, in which false, inaccurate, or deceptive information is spread and shared by online users. False information is not limited to verifiable information, but it also involves information that is used for harmful purposes. Also, one of the challenges that researchers have to face is the massive number of users in social media platforms, where detecting false information spreaders is not an easy job. Previous work that has been proposed for limiting or studying the issue of detecting false information has focused on understanding the language of false information from a linguistic perspective. In the case of verifiable information, approaches have been proposed in a monolingual setting. Moreover, detecting the sources or the spreaders of false information in social media has not been investigated much. In this thesis we study false information from several aspects. First, since previous work focused on studying false information in a monolingual setting, in this thesis we study false information in a cross-lingual one. We propose different cross-lingual approaches and we compare them to a set of monolingual baselines. Also, we provide systematic studies for the evaluation results of our approaches for better understanding. Second, we noticed that the role of affective information was not investigated in depth. Therefore, the second part of our research work studies the role of the affective information in false information and shows how the authors of false content use it to manipulate the reader. Here, we investigate several types of false information to understand the correlation between affective information and each type (Propaganda, Hoax, Clickbait, Rumor, and Satire). Last but not least, in an attempt to limit its spread, we also address the problem of detecting false information spreaders in social media. In this research direction, we focus on exploiting several text-based features extracted from the online profile messages of those spreaders. We study different feature sets that can have the potential to help to identify false information spreaders from fact checkers.Ghanem, BHH. (2020). On the Detection of False Information: From Rumors to Fake News [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/158570TESISCompendi