Memory-induced complex contagion in spreading phenomena on networks

[eng] Epidemic modeling has proven to be an essential framework for the study of contagion phenomena in biological, social, and technical systems. Albeit epidemic models have evolved into powerful predictive tools, most assume memoryless agents and independent transmission channels. Nevertheless, many real-life examples are manifestly time-sensitive and show strong correlations. Moreover, recent trends in agent-based modeling support a generalized shift from edge-based descriptions toward node-centric approaches. Here I develop an infection mechanism that is endowed with memory of past exposures and simultaneously incorporates the joint effect of multiple infectious sources. A notion of social reinforcement/inhibition arises organically, without being incorporated explicitly into the model. As a result, the concepts of non-Markovian dynamics and complex contagion become intrinsically coupled. I derive mean-field approximations for random degree-regular networks and perform extensive stochastic simulations for nonhomogeneous networks. The analysis of the SIS model reveals a sophisticated interplay between two memory modes, displayed by a collective memory loss and the dislocation of the critical point into two phase transitions. An intermediate region emerges where the system is either excitable or bistable, exhibiting fundamentally distinct behaviors compared to the customary healthy and endemic phases. Additionally, the transition to the endemic phase becomes hybrid, showing both continuous and discontinuous properties. These results provide renewed insights on the interaction between microscopic mechanisms and topological aspects of the underlying contact networks, and their joint effect on the properties of spreading processes. In particular, this type of modeling approach that combines memory effects and complex contagion could be suitable to describe ecological interactions between biological and social pathogens.[cat] El modelatge epidèmic ha demostrat ser un marc essencial per a l’estudi dels fenòmens de contagi en sistemes biològics, socials i tècnics. Tot i que els models epidèmics han evolucionat cap a potents eines de predicció, la majoria assumeixen agents sense memòria i canals de transmissió independents. No obstant això, molts exemples de la vida real mostren fortes correlacions temporals i estructurals. A més, les tendències recents en la modelització basada en agents donen suport a un canvi generalitzat de les descripcions basades en els enllaços cap a enfocaments on els nodes són centrals. Aquí desenvolupo un mecanisme d’infecció dotat de memòria a exposicions passades i que simultàniament incorpora l’efecte conjunt de múltiples fonts infeccioses. Una noció de reforç/inhibició social sorgeix de manera orgànica, sense incorporar-se explícitament al model. Com a resultat, els conceptes de dinàmica no markoviana i contagi complex s’acoblen intrínsecament. Derivo aproximacions de camp mitjà per a xarxes aleatòries de grau fix i realitzo extenses simulacions estocàstiques per a xarxes no homogènies. L'anàlisi del model SIS revela una interacció sofisticada entre dos modes de memòria, que es manifesta mitjançant una pèrdua de memòria col·lectiva i la dislocació del punt crític en dues transicions de fase. Apareix una regió intermitja on el sistema és excitable o bistable, amb comportaments fonamentalment diferents en comparació amb les fases sanes i endèmiques habituals. A més, la transició a la fase endèmica esdevé híbrida, mostrant propietats contínues i també discontínues. Aquests resultats proporcionen una visió renovada sobre la interacció entre mecanismes microscòpics i aspectes topològics de les xarxes de contacte subjacents, i el seu efecte conjunt sobre les propietats dels processos de propagació. En particular, aquest tipus de modelització que combina efectes de memòria i contagi complex podria ser adequat per descriure interaccions ecològiques entre patògens biològics i socials.[spa] El modelado epidémico ha demostrado ser un marco esencial para el estudio de los fenómenos de contagio en sistemas biológicos, sociales y técnicos. Aunque los modelos epidémicos han evolucionado hacia potentes herramientas de predicción, la mayoría asumen agentes sin memoria y canales de transmisión independientes. Sin embargo, muchos ejemplos de la vida real muestran fuertes correlaciones temporales y estructurales. Además, las tendencias recientes en la modelización basada en agentes apoyan un cambio generalizado de las descripciones basadas en los enlaces hacia enfoques donde los nodos son centrales. Aquí desarrollo un mecanismo de infección dotado de memoria a exposiciones pasadas y que simultáneamente incorpora el efecto conjunto de múltiples fuentes infecciosas. Una noción de refuerzo/inhibición social surge de manera orgánica, sin incorporarse explícitamente al modelo. Como resultado, los conceptos de dinámica no Markoviana y contagio complejo se acoplan intrínsecamente. Derivo aproximaciones de campo medio para redes aleatorias de grado fijo y realizo extensas simulaciones estocásticas para redes no homogéneas. El análisis del modelo SIS revela una interacción sofisticada entre dos modos de memoria, que se manifiesta mediante una pérdida de memoria colectiva y la dislocación del punto crítico en dos transiciones de fase. Aparece una región intermedia donde el sistema es excitable o bistable, con comportamientos fundamentalmente diferentes en comparación con las fases sanas y endémicas habituales. Además, la transición a la fase endémica se convierte en híbrida, mostrando propiedades continuas y también discontinuas. Estos resultados proporcionan una visión renovada sobre la interacción entre mecanismos microscópicos y aspectos topológicos de las redes de contacto subyacentes, y su efecto conjunto sobre las propiedades de los procesos de propagación. En particular, este tipo de modelización que combina efectos de memoria y contagio complejo podría ser adecuado para describir interacciones ecológicas entre patógenos biológicos y sociales

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

Human mobility:Models and applications

Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.Comment: 126 pages, 45+ figure

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201

Human mobility: Models and applications

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordRecent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.US Army Research Offic

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

Systemic propagation of delays in the air-transportation network

Tesis doctoral presentada por Pablo Fleurquin para optar al título de Doctor, en el Programa de Física del Departamento de Física de la Universitat de les Illes Balears, realizada en el IFISC bajo la dirección de José Javier Ramasco Sukia, Víctor Martinez Eguíluz y como ponente Maxi San Miguel Ruibal.[EN] The focus of this dissertation is to quantitative describe, analyze and model a paradigmatic socio-technical complex system such as the air-transportation system. The generation, propagation and eventual amplification of flight de- lays involve a large number of interacting mechanisms. Such mechanisms can be classified as internal or external to the air tra c system. The basic internal mechanisms include aircraft rotations (the di erent flight legs that comprise an aircraft itinerary), airport operations, passengers’ connections and crew rotation. In addition, external factors, such as weather perturbations or security threats, disturb the system performance and contribute to a high level of system-wide congestion. Although this socio-technical system is driven by human decisions, the intricacy of the interactions between all these elements calls for an analy- sis of flight delays under the scope of Complex Systems theory. Complexity is concerned with the emergence of collective behavior from the microscopic interaction of the system elements. Several tools have been developed to tackle complexity. Here we use Complex Networks theory and take a system-wide perspective to broaden the understanding of delay propagation.Peer reviewe

Negotiation Based Resource Allocation to Control Information Diffusion

Study of diffusion or propagation of information over a network of connected entities play a vital role in understanding and analyzing the impact of such diffusion, in particular, in the context of epidemiology, and social and market sciences. Typical concerns addressed by these studies are to control the diffusion such that influence is maximally (in case of opinion propagation) or minimally (in case of infectious disease) felt across the network. Controlling diffusion requires deployment of resources and often availability of resources are socio-economically constrained. In this context, we propose an agent-based framework for resource allocation, where agents operate in a cooperative environment and each agent is responsible for identifying and validating control strategies in a network under its control. The framework considers the presence of a central controller that is responsible for negotiating with the agents and allocate resources among the agents. Such assumptions replicates real-world scenarios, particularly in controlling infection spread, where the resources are distributed by a central agency (federal govt.) and the deployment of resources are managed by a local agency (state govt.). If there exists an allocation that meets the requirements of all the agents, our framework is guaranteed to find one such allocation. While such allocation can be obtained in a blind search methods (such as checking the minimum number of resources required by each agent or by checking allocations between each pairs), we show that considering the responses from each agent and considering allocation among all the agents results in a “negotiation” based technique that converges to a solution faster than the brute force methods. We evaluated our framework using data publicly available from Stanford Network Analysis Project to simulate different types of networks for each agents