Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms

This monograph provides an overview of the mathematical theories and computational algorithm design for contagion source detection in large networks. By leveraging network centrality as a tool for statistical inference, we can accurately identify the source of contagions, trace their spread, and predict future trajectories. This approach provides fundamental insights into surveillance capability and asymptotic behavior of contagion spreading in networks. Mathematical theory and computational algorithms are vital to understanding contagion dynamics, improving surveillance capabilities, and developing effective strategies to prevent the spread of infectious diseases and misinformation.Comment: Suggested Citation: Chee Wei Tan and Pei-Duo Yu (2023), "Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms", Foundations and Trends in Networking: Vol. 13: No. 2-3, pp 107-251. http://dx.doi.org/10.1561/130000006

Approximation Algorithms for Broadcasting in Simple Graphs with Intersecting Cycles

Broadcasting is an information dissemination problem in a connected network in which one node, called the originator, must distribute a message to all other nodes by placing a series of calls along the communication lines of the network. Every time the informed nodes aid the originator in distributing the message. Finding the minimum broadcast time of any vertex in an arbitrary graph is NP-Complete. The problem remains NP-Complete even for planar graphs of degree 3 and for a graph whose vertex set can be partitioned into a clique and an independent set. The best theoretical upper bound gives logarithmic approximation. It has been shown that the broadcasting problem is NP-Hard to approximate within a factor of 3-ɛ. The polynomial time solvability is shown only for tree-like graphs; trees, unicyclic graphs, tree of cycles, necklace graphs and some graphs where the underlying graph is a clique; such as fully connected trees and tree of cliques. In this thesis we study the broadcast problem in different classes of graphs where cycles intersect in at least one vertex. First we consider broadcasting in a simple graph where several cycles have common paths and two intersecting vertices, called a k-path graph. We present a constant approximation algorithm to find the broadcast time of an arbitrary k-path graph. We also study the broadcast problem in a simple cactus graph called k-cycle graph where several cycles of arbitrary lengths are connected by a central vertex on one end. We design a constant approximation algorithm to find the broadcast time of an arbitrary k-cycle graph. Next we study the broadcast problem in a hypercube of trees for which we present a 2-approximation algorithm for any originator. We provide a linear algorithm to find the broadcast time in hypercube of trees with one tree. We extend the result for any arbitrary graph whose nodes contain trees and design a linear time constant approximation algorithm where the broadcast scheme in the arbitrary graph is already known. In Chapter 6 we study broadcasting in Harary graph for which we present an additive approximation which gives 2-approximation in the worst case to find the broadcast time in an arbitrary Harary graph. Next for even values of n, we introduce a new graph, called modified-Harary graph and present a 1-additive approximation algorithm to find the broadcast time. We also show that a modified-Harary graph is a broadcast graph when k is logarithmic of n. Finally we consider a diameter broadcast problem where we obtain a lower bound on the broadcast time of the graph which has at least (d+k-1 choose d) + 1 vertices that are at a distance d from the originator, where k >= 1

On the robustness of the metric dimension of grid graphs to adding a single edge

The metric dimension (MD) of a graph is a combinatorial notion capturing the minimum number of landmark nodes needed to distinguish every pair of nodes in the graph based on graph distance. We study how much the MD can increase if we add a single edge to the graph. The extra edge can either be selected adversarially, in which case we are interested in the largest possible value that the MD can take, or uniformly at random, in which case we are interested in the distribution of the MD. The adversarial setting has already been studied by [Eroh et. al., 2015] for general graphs, who found an example where the MD doubles on adding a single edge. By constructing a different example, we show that this increase can be as large as exponential. However, we believe that such a large increase can occur only in specially constructed graphs, and that in most interesting graph families, the MD at most doubles on adding a single edge. We prove this for $d$-dimensional grid graphs, by showing that $2d$ appropriately chosen corners and the endpoints of the extra edge can distinguish every pair of nodes, no matter where the edge is added. For the special case of $d=2$, we show that it suffices to choose the four corners as landmarks. Finally, when the extra edge is sampled uniformly at random, we conjecture that the MD of 2-dimensional grids converges in probability to $3+\mathrm{Ber}(8/27)$, and we give an almost complete proof

Fundamentals of spreading processes in single and multilayer complex networks

Spreading processes have been largely studied in the literature, both analytically and by means of large-scale numerical simulations. These processes mainly include the propagation of diseases, rumors and information on top of a given population. In the last two decades, with the advent of modern network science, we have witnessed significant advances in this field of research. Here we review the main theoretical and numerical methods developed for the study of spreading processes on complex networked systems. Specifically, we formally define epidemic processes on single and multilayer networks and discuss in detail the main methods used to perform numerical simulations. Throughout the review, we classify spreading processes (disease and rumor models) into two classes according to the nature of time: (i) continuous-time and (ii) cellular automata approach, where the second one can be further divided into synchronous and asynchronous updating schemes. Our revision includes the heterogeneous mean-field, the quenched-mean field, and the pair quenched mean field approaches, as well as their respective simulation techniques, emphasizing similarities and differences among the different techniques. The content presented here offers a whole suite of methods to study epidemic-like processes in complex networks, both for researchers without previous experience in the subject and for experts.Comment: Review article. 73 pages, including 24 figure

Robust distributed data aggregation

Tese de doutoramento Programa Doutoral em Informática MAP-iDistributed aggregation algorithms are an important building block of modern large scale systems, as it allows the determination of meaningful system-wide properties (e.g., network size, total storage capacity, average load, or majorities) which are required to direct the execution of distributed applications. In the last decade, several algorithms have been proposed to address the distributed computation of aggregation functions (e.g., COUNT, SUM, AVERAGE, and MAX/MIN), exhibiting different properties in terms of accuracy, speed and communication tradeoffs. However, existing approaches exhibit many issues when challenged in faulty and dynamic environments, lacking in terms of fault-tolerance and support to churn. This study details a novel distributed aggregation approach, named Flow Updating, which is fault-tolerant and able to operate on dynamics networks. The algorithm is based on manipulating flows (inspired by the concept from graph theory), that are updated using idempotent messages, providing it with unique robustness capabilities. Experimental results showed that Flow Updating outperforms previous averaging algorithms in terms of time and message complexity, and unlike them it self adapts to churn and changes of the initial input values without requiring any periodic restart, supporting node crashes and high levels of message loss. In addition to this main contribution, others can also be found in this research work, namely: a definition of the aggregation problem is proposed; existing distributed aggregation algorithm are surveyed and classified into a comprehensive taxonomy; a novel algorithm is introduced, based on Flow Updating, to estimate the Cumulative Distribution Function (CDF) of a global system attribute. It is expected that this work will constitute a relevant contribution to the area of distributed computing, in particular to the robust distributed computation of aggregation functions in dynamic networks.Os algoritmos de agregação distribuídos têm um papel importante no desenho dos sistemas de larga escala modernos, uma vez que permitem determinar o valor de propriedades globais do sistema (e.g., tamanho da rede, capacidade total de armazenamento, carga média, ou maiorias) que são fundamentais para a execução de outras aplicações distribuídas. Ao longo da última década, diversos algoritmos têm sido propostos para calcular funções de agregação (e.g., CONTAGEM, SOMA, M´E DIA, ou MIN/MAX), possuindo diferentes características em termos de precisão, velocidade e comunicação. No entanto, as técnicas existentes exibem vários problemas quando executadas em ambientes com faltas e dinâmicos, deixando a desejar em termos de tolerância a faltas e não suportando a entrada/saída de nós. Este estudo descreve detalhadamente uma nova abordagem para calcular funções de agregação de forma distribuída, denominada Flow Updating, que é tolerante a faltas e capaz de operar em redes dinámicas. O algoritmo é baseada na manipulacão de fluxos (inspirado no conceito da teoria de grafos), que são atualizados por mensagens idempotentes, conferindo-lhe capacidades unicas em termos de robustez. Os resultados experimentais demonstram que o Flow Updating supera os anteriores algoritmos baseados em técnicas de averaging em termos de complexidade de tempo e mensagens, e, ao contrário destes, auto adapta-se a mudanc¸as da rede (i.e., entrada/saída de nós e alteraçãoo dos valores iniciais) sem necessitar de reiniciar periodicamente a sua execuçãoo, suportando falhas de nos e elevados níveis de perdas de mensagens. Para além desta contribuição principal, outras são também encontradas neste trabalho, nomeadamente: é proposta uma definição do problema da agregação; é descrito o estado da arte em termos dos algoritmos de agregação distribuídos, e estes são classificados numa taxonomia abrangente; é apresentado um novo algoritmo baseado no Flow Updating para estimar a Funcão de Distribuição Cumulativa (CDF) de um atributo global do sistema

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum