Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure

Contagion in an interacting economy

We investigate the credit risk model defined in Hatchett & K\"{u}hn under more general assumptions, in particular using a general degree distribution for sparse graphs. Expanding upon earlier results, we show that the model is exactly solvable in the $N\rightarrow \infty$ limit and demonstrate that the exact solution is described by the message-passing approach outlined by Karrer and Newman, generalized to include heterogeneous agents and couplings. We provide comparisons with simulations of graph ensembles with power-law degree distributions.Comment: 21 pages, 6 figure

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

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

Influence Maximization based on Simplicial Contagion Model in Hypergraph

In recent years, the issue of node centrality has been actively and extensively explored due to its applications in product recommendations, opinion propagation, disease spread, and other areas involving maximizing node influence. This paper focuses on the problem of influence maximization on the Simplicial Contagion Model, using the susceptible-infectedrecovered (SIR) model as an example. To find practical solutions to this optimization problem, we have developed a theoretical framework based on message passing processes and conducted stability analysis of equilibrium solutions for the self-consistent equations. Furthermore, we introduce a metric called collective influence and propose an adaptive algorithm, known as the Collective Influence Adaptive (CIA), to identify influential propagators in the spreading process. This method has been validated on both synthetic hypergraphs and real hypergraphs, outperforming other competing heuristic methods.Comment: 19 pages,16 figure

The role of ADAM10, ADAM17, and Spag6 in humoral immunity and secondary lymphoid tissue architecture

ADAM10, ADAM17, and SPAG6 contribute significantly to humoral immunity and secondary lymphoid tissue architecture. ADAM10 and ADAM17 are two closely related zinc-metalloproteinases. Through cleavage of their ligands CD23 and TNF, respectively, they greatly influence IgE production and secondary lymphoid tissue architecture maintenance. Th1 prone WT strains initially exhibit increased ADAM17 and TNF yet reduced ADAM10 relative to Th2 prone WT strains. In the absence of B cell ADAM10, a compensatory increase in ADAM17 and TNF cleavage is noted only in Th1 prone C57Bl/6, not Th2 prone Balb/c. B cell TNF homeostasis is important for maintaining secondary lymphoid tissue architecture. We show for the first time that excessive B cell TNF production in C57-ADAM10B-/- lymph nodes contributes to loss of B/T segregation, increased HEV number and size, fibrosis, loss of FDC networks, and impaired germinal center formation. Furthermore, B cell ADAM10, which enhances IgE production through CD23 cleavage, is shown to be a marker of Th2 susceptibility. B cell ADAM10 is elevated in Th2 prone mouse strains and allergic patients compared to Th1 prone controls and as B cell ADAM10 level increases, so does IgE production. Lastly, the B cell profile of allergic patients is determined to be B cell ADAM10highADAM17lowTNFlow. Furthermore, the mechanism underlying reduced class-switched antibody production in C57-ADAM10B-/- mice is explored. C57-ADAM10B-/- B cells exhibit a B10, or IL-10 producing, phenotype, which is linked to reduced antibody production. Furthermore, increased Tregs noted in C57-ADAM10B-/- mice contributed to reduced class switched IgE production and disease parameters following a house dust mite airway inflammation challenge. SPAG6, a component of the central apparatus of the “9+2” axoneme, plays a central role in flagellar stability and motility. Immune cells lack cilia, but the immunological synapse is a surrogate cilium as it utilizes the same machinery as ciliogenesis including the nucleation of microtubules at the centrosome. We demonstrate that Spag6 localizes in the centrosome and is critical for centrosome polarization at and actin clearance away from the synapse between CTL and target cells. Furthermore, improper synapse formation and function likely explains reduced CTL function and class-switched antibody production in Spag6KO mice

BRAC health informatics system

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2005.Cataloged from PDF version of thesis report.Includes bibliographical references (page 102).At the beginning of the 21st century, the field of global public health is changing rapidly, not only in its basic methods, but also in technological aspects. The first and foremost concerns of BRAC health program is to provide health service to mass populations. To cope up with changing world’s need BRAC Health department should accept the fruit of technology. As a result we have proposed three solutions to automate the entire health peocess namely- (I) using hand scanner, mobile phone and OCR technology, (II) using Epi Info software package tools for data analysis, (III) web-based database system. This report focuses on automation using hand scanner, mobile phone and OCR technology. It offers real time data usability and scope for analysis. Thus provides rapid and accurate decision making opportunity.Hasnain FerozeSoriful Alam SumonB. Computer Science and Engineerin