Towards real-world complexity: an introduction to multiplex networks

Many real-world complex systems are best modeled by multiplex networks of interacting network layers. The multiplex network study is one of the newest and hottest themes in the statistical physics of complex networks. Pioneering studies have proven that the multiplexity has broad impact on the system's structure and function. In this Colloquium paper, we present an organized review of the growing body of current literature on multiplex networks by categorizing existing studies broadly according to the type of layer coupling in the problem. Major recent advances in the field are surveyed and some outstanding open challenges and future perspectives will be proposed.Comment: 20 pages, 10 figure

On the use of generating functions for topics in clustered networks

In this thesis we relax the locally tree-like assumption of configuration model random networks to examine the properties of clustering, and the effects thereof, on bond percolation. We introduce an algorithmic enumeration method to evaluate the probability that a vertex remains unattached to the giant connected component during percolation. The properties of the non-giant, finite components of clustered networks are also examined, along with the degree correlations between subgraphs. In a second avenue of research, we investigate the role of clustering on 2-strain epidemic processes under various disease interaction schedules. We then examine an -generation epidemic by performing repeated percolation events

Manipulating concept spread using concept relationships

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread

Spreading processes over multilayer and interconnected networks

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringCaterina ScoglioSociety increasingly depends on networks for almost every aspect of daily life. Over the past decade, network science has flourished tremendously in understanding, designing, and utilizing networks. Particularly, network science has shed light on the role of the underlying network topology on the dynamic behavior of complex systems, including cascading failure in power-grids, financial contagions in trade market, synchronization, spread of social opinion and trends, product adoption and market penetration, infectious disease pandemics, outbreaks of computer worms, and gene mutations in biological networks. In the last decade, most studies on complex networks have been confined to a single, often homogeneous network. An extremely challenging aspect of studying these complex systems is that the underlying networks are often heterogeneous, composite, and interdependent with other networks. This challenging aspect has very recently introduced a new class of networks in network science, which we refer to as multilayer and interconnected networks. Multilayer networks are an abstract representation of interconnection among nodes representing individuals or agents, where the interconnection has a multiple nature. For example, while a disease can propagate among individuals through a physical contact network, information can propagate among the same individuals through an online information-dissemination network. Another example is viral information dissemination among users of online social networks; one might disseminate information received from a Facebook contact to his or her followers on Twitter. Interconnected networks are abstract representations where two or more simple networks, possibly with different dynamics over them, are interconnected to each other. For example, in zoonotic diseases, a virus can move from the network of animals, with some transmission dynamics, to a human network, with possibly very different dynamics. As communication systems are evolving more and more toward integration with computing, sensing, and control systems, the theory of multilayer and interconnected networks seems to be crucial to successful communication systems development in cyber-physical infrastructures. Among the most relevant dynamics over networks is epidemic spreading. Epidemic spreading dynamics over simple networks exhibit a clear example where interaction between non-complex dynamics at node level and the topology leads to a complex emergent behavior. A substantial line of research during the past decade has been devoted to capturing the role of the network on spreading dynamics, and mathematical tools such as spectral graph theory have been greatly useful for this goal. For example, when the network is a simple graph, the dominant eigenvalue and eigenvector of the adjacency matrix have been proven to be key elements determining spreading dynamics features, including epidemic threshold, centrality of nodes, localization of spreading sites, and behavior of the epidemic model close to the threshold. More generally, for many other dynamics over a single network, dependency of dynamics on spectral properties of the adjacency matrix, Laplacian matrix, or some other graph-related matrix, is well-studied and rigorously established, and practical applications have been successfully derived. In contrast, limited established results exist for dynamics on multilayer and interconnected networks. Yet, an understanding of spreading processes over these networks is very important to several realistic phenomena in modern integrated and composite systems, including cascading failure in power grids, financial contagions in trade market, synchronization, spread of social opinion and trends, product adoption and market penetration, infectious disease pandemics, and outbreak in computer worms. This dissertation focuses on spreading processes on multilayer and interconnected networks, organized in three parts. The first part develops a general framework for modeling epidemic spreading in interconnected and multilayer networks. The second part solves two fundamental problems: introducing the concept of an epidemic threshold curve in interconnected networks, and coexistence phenomena in competitive spreading over multilayer networks. The third part of this dissertation develops an epidemic model incorporating human behavior, where multi-layer network formulation enables modeling and analysis of important features of human social networks, such as an information-dissemination network, as well as contact adaptation. Finally, I conclude with some open research directions in the topic of spreading processes over multilayer and interconnected networks, based on the resulting developments of this dissertation

Virus host shifts in Drosophila: The influences of virus genotype and coinfection on susceptibility within and across host species

Virus host shifts are a major source of outbreaks and emerging infectious diseases, and continue to cause considerable damage to public health, society, and the global economy. Predicting and preventing future virus host shifts has become a primary goal of infectious disease research, and multiple tools and approaches are being developed to work towards this goal. In this thesis, I examine three key aspects of infection that have implications for our wider understanding of virus host shifts and their predictability in natural systems: whether the outcome of infections across species is correlated between related viruses, whether the presence of a coinfecting virus can alter the outcomes of cross-species transmission, and the influence of host genetics and immunity on the outcomes of coinfection. These experiments make use of a large and evolutionarily diverse panel of Drosphilidae host species, and infections with two insect Cripaviruses: Drosophila C virus (DCV) and Cricket Paralysis virus (CrPV), with the outcomes of infection quantified throughout as viral loads via qRT-PCR. In Chapter Two, phylogenetic generalised linear mixed models are applied to data on the outcome of single infections with three isolates of DCV (DCV-C, DCV-EB, DCV-M) and one isolate of CrPV, to look for correlations in viral load across host species. Strong positive corrections were found between DCV isolates and weaker positive correlations between DCV and CrPV, with evidence of host species by virus interactions on the outcome of infection. Of the four viruses tested, the most closely related isolates tended to be the most strongly correlated, with correlation strength deteriorating with the evolutionary distance between isolates, although we lacked the diversity or sample size of viruses to properly determine any effect of evolutionary distance on correlation strength. Together, this suggests that hosts susceptible to one virus are also susceptible to closely related viruses, and that knowledge of one virus may be extrapolated to closely related viruses, at least within the range of evolutionary divergence tested here. In the remainder of this thesis, I examine the outcome of coinfection with DCV-C and CrPV across host species (Chapter Three) and across genotypes and immune mutants of Drosophila melanogaster (Chapter Four). These chapters aim to assess the potential for coinfection to alter the outcomes of cross-species transmission – and so interfere with predictions of virus host shifts – and the potential influence of host genetics and immunity on the outcome of coinfection. Chapter Three finds little evidence of systematic changes in the outcome of single and coinfection for both viruses across species, suggesting that coinfection may not be a required consideration in predictive models of every host-virus system. Effects of coinfection were found in a subset of species but were not recapitulated in a follow-up experiment looking at tissue tropism during coinfection on a subset of host species. Together, this suggests that any effects of coinfection across species with DCV and CrPV are due to stochastic effects within individual hosts. Chapter Four finds small but credible effects of coinfection across genotypes of D. melanogaster, but these effects showed little host genetic basis or effect on the genetic basis of susceptibility to each virus separately. Mutations in several immune genes caused virus-specific changes in viral load between single and coinfection, suggesting that coinfection interactions between viruses can be moderated by the host immune response. This thesis has aimed to explore several fundamental features of cross-species transmission that are relevant to our understanding – and ability to predict – virus host shifts. Both the finding that correlations exist between viruses and the approach used to characterise coinfection across and within host species would now benefit from an increased diversity of experimental pathogens, to better investigate the influence of virus evolutionary relationships on the outcomes of virus host shifts and present a broader understanding of the potential impact of coinfection on the outcomes of cross-species transmission.Natural Environment Research Council (NERC

Pathogen Interactions in Co-infected Wheat Determine Disease Ontogeny and Severity

This PhD bridges the gap between the heterogeneity of plant diseases in the field and the emerging field of molecular pathology. It uses a three-way system consisting of the wheat host and two fungi. A range of techniques relating to plant pathology and histology as well as advanced molecular biology and cytology were used. The objective of the thesis was to study disease progress and pathogen interactions in wheat plants co-infected by two major fungi

2015 IMSAloquium, Student Investigation Showcase

We want to express our gratitude for the generosity and steadfast support of all the experts and leaders who have nurtured These collaborative partnerships are the strength of our SIR program.https://digitalcommons.imsa.edu/archives_sir/1014/thumbnail.jp