Optimal Control of Epidemics in the Presence of Heterogeneity

We seek to identify and address how different types of heterogeneity affect the optimal control of epidemic processes in social, biological, and computer networks. Epidemic processes encompass a variety of models of propagation that are based on contact between agents. Assumptions of homogeneity of communication rates, resources, and epidemics themselves in prior literature gloss over the heterogeneities inherent to such networks and lead to the design of sub-optimal control policies. However, the added complexity that comes with a more nuanced view of such networks complicates the generalizing of most prior work and necessitates the use of new analytical methods. We first create a taxonomy of heterogeneity in the spread of epidemics. We then model the evolution of heterogeneous epidemics in the realms of biology and sociology, as well as those arising from practice in the fields of communication networks (e.g., DTN message routing) and security (e.g., malware spread and patching). In each case, we obtain computational frameworks using Pontryagin’s Maximum Principle that will lead to the derivation of dynamic controls that optimize general, context-specific objectives. We then prove structures for each of these vectors of optimal controls that can simplify the derivation, storage, and implementation of optimal policies. Finally, using simulations and real-world traces, we examine the benefits achieved by including heterogeneity in the control decision, as well as the sensitivity of the models and the controls to model parameters in each case

NDlib: a python library to model and analyze diffusion processes over complex networks

Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians

Stochastic spreading on complex networks

Complex interacting systems are ubiquitous in nature and society. Computational modeling of these systems is, therefore, of great relevance for science and engineering. Complex networks are common representations of these systems (e.g., friendship networks or road networks). Dynamical processes (e.g., virus spreading, traffic jams) that evolve on these networks are shaped and constrained by the underlying connectivity. This thesis provides numerical methods to study stochastic spreading processes on complex networks. We consider the processes as inherently probabilistic and analyze their behavior through the lens of probability theory. While powerful theoretical frameworks (like the SIS-epidemic model and continuous-time Markov chains) already exist, their analysis is computationally challenging. A key contribution of the thesis is to ease the computational burden of these methods. Particularly, we provide novel methods for the efficient stochastic simulation of these processes. Based on different simulation studies, we investigate techniques for optimal vaccine distribution and critically address the usage of mathematical models during the Covid-19 pandemic. We also provide model-reduction techniques that translate complicated models into simpler ones that can be solved without resorting to simulations. Lastly, we show how to infer the underlying contact data from node-level observations.Komplexe, interagierende Systeme sind in Natur und Gesellschaft allgegenwärtig. Die computergestützte Modellierung dieser Systeme ist daher von immenser Bedeutung für Wissenschaft und Technik. Netzwerke sind eine gängige Art, diese Systeme zu repräsentieren (z. B. Freundschaftsnetzwerke, Straßennetze). Dynamische Prozesse (z. B. Epidemien, Staus), die sich auf diesen Netzwerken ausbreiten, werden durch die spezifische Konnektivität geformt. In dieser Arbeit werden numerische Methoden zur Untersuchung stochastischer Ausbreitungsprozesse in komplexen Netzwerken entwickelt. Wir betrachten die Prozesse als inhärent probabilistisch und analysieren ihr Verhalten nach wahrscheinlichkeitstheoretischen Fragestellungen. Zwar gibt es bereits theoretische Grundlagen und Paradigmen (wie das SIS-Epidemiemodell und zeitkontinuierliche Markov-Ketten), aber ihre Analyse ist rechnerisch aufwändig. Ein wesentlicher Beitrag dieser Arbeit besteht darin, die Rechenlast dieser Methoden zu verringern. Wir erforschen Methoden zur effizienten Simulation dieser Prozesse. Anhand von Simulationsstudien untersuchen wir außerdem Techniken für optimale Impfstoffverteilung und setzen uns kritisch mit der Verwendung mathematischer Modelle bei der Covid-19-Pandemie auseinander. Des Weiteren führen wir Modellreduktionen ein, mit denen komplizierte Modelle in einfachere umgewandelt werden können. Abschließend zeigen wir, wie man von Beobachtungen einzelner Knoten auf die zugrunde liegende Netzwerkstruktur schließt

DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES

This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation

Dictionary of privacy, data protection and information security

The Dictionary of Privacy, Data Protection and Information Security explains the complex technical terms, legal concepts, privacy management techniques, conceptual matters and vocabulary that inform public debate about privacy. The revolutionary and pervasive influence of digital technology affects numerous disciplines and sectors of society, and concerns about its potential threats to privacy are growing. With over a thousand terms meticulously set out, described and cross-referenced, this Dictionary enables productive discussion by covering the full range of fields accessibly and comprehensively. In the ever-evolving debate surrounding privacy, this Dictionary takes a longer view, transcending the details of today''s problems, technology, and the law to examine the wider principles that underlie privacy discourse. Interdisciplinary in scope, this Dictionary is invaluable to students, scholars and researchers in law, technology and computing, cybersecurity, sociology, public policy and administration, and regulation. It is also a vital reference for diverse practitioners including data scientists, lawyers, policymakers and regulators

Information Diffusion and Summarization in Social Networks

Social networks are web-based services that allow users to connect and share information. Due to the huge size of social network graph and the plethora of generated content, it is difficult to diffuse and summarize the social media content. This thesis thus addresses the problems of information diffusion and information summarization in social networks. Information diffusion is a process by which information about new opinions, behaviors, conventions, practices, and technologies flow from person-to-person through a social network. Studies on information diffusion primarily focus on how information diffuses in networks and how to enhance information diffusion. Our aim is to enhance the information diffusion in social networks. Many factors affect information diffusion, such as network connectivity, location, posting timestamp, post content, etc. In this thesis, we analyze the effect of three of the most important factors of information diffusion, namely network connectivity, posting time and post content. We first study the network factor to enhance the information diffusion, and later analyze how time and content factors can diffuse the information to a large number of users. Network connectivity of a user determines his ability to disseminate information. A well-connected authoritative user can disseminate information to a more wider audience compared to an ordinary user. We present a novel algorithm to find topicsensitive authorities in social networks. We use the topic-specific authoritative position of the users to promote a given topic through word-of-mouth (WoM) marketing. Next, the lifetime of social media content is very short, which is typically a few hours. If post content is posted at the time when the targeted audience are not online or are not interested in interacting with the content, the content will not receive high audience reaction. We look at the problem of finding the best posting time(s) to get high information diffusion. Further, the type of social media content determines the amount of audience interaction, it gets in social media. Users react differently to different types of content. If a post is related to a topic that is more arousing or debatable, then it tends to get more comments. We propose a novel method to identify whether a post has high arousal content or not. Furthermore, the sentiment of post content is also an important factor to garner users’ attention in social media. Same information conveyed with different sentiments receives a different amount of audience reactions. We understand to what extent the sentiment policies employed in social media have been successful to catch users’ attention. Finally, we study the problem of information summarization in social networks. Social media services generate a huge volume of data every day, which is difficult to search or comprehend. Information summarization is a process of creating a concise readable summary of this huge volume of unstructured information. We present a novel method to summarize unstructured social media text by generating topics similar to manually created topics. We also show a comprehensive topical summary by grouping semantically related topics

Emerging Technologies

This monograph investigates a multitude of emerging technologies including 3D printing, 5G, blockchain, and many more to assess their potential for use to further humanity’s shared goal of sustainable development. Through case studies detailing how these technologies are already being used at companies worldwide, author Sinan Küfeoğlu explores how emerging technologies can be used to enhance progress toward each of the seventeen United Nations Sustainable Development Goals and to guarantee economic growth even in the face of challenges such as climate change. To assemble this book, the author explored the business models of 650 companies in order to demonstrate how innovations can be converted into value to support sustainable development. To ensure practical application, only technologies currently on the market and in use actual companies were investigated. This volume will be of great use to academics, policymakers, innovators at the forefront of green business, and anyone else who is interested in novel and innovative business models and how they could help to achieve the Sustainable Development Goals. This is an open access book