Information diffusion in interconnected heterogeneous networks

In this paper, we are interested in modeling the diffusion of information in a multilayer network using thermodynamic diffusion approach. State of each agent is viewed as a topic mixture represented by a distribution over multiple topics. We have observed and learned diffusion-related thermodynamical patterns in the training data set, and we have used the estimated diffusion structure to predict the future states of the agents. A priori knowledge of a fraction of the state of all agents changes the problem to be a Kalman predictor problem that refines the predicted system state using the error in estimation of the agents. A real world Twitter data set is then used to evaluate and validate our information diffusion model.Comment: 5-9 March 2017. arXiv admin note: substantial text overlap with arXiv:1602.0485

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table

An anonymous inter-network routing protocol for the Internet of Things

With the diffusion of the Internet of Things (IoT), computing is becoming increasingly pervasive, and different heterogeneous networks are integrated into larger systems. However, as different networks managed by different parties and with different security requirements are interconnected, security becomes a primary concern. IoT nodes, in particular, are often deployed “in the open”, where an attacker can gain physical access to the device. As nodes can be deployed in unsurveilled or even hostile settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore necessary to secure the communication within IoT networks, and in particular, maintain context information private, including the network topology and the location and identity of the nodes. In this paper, we propose a protocol achieving anonymous routing between different interconnected networks, designed for the Internet of Things and based on the spatial Bloom filter (SBF) data structure. The protocol enables private communication between the nodes through the use of anonymous identifiers, which hide their location and identity within the network. As routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain, the proposed routing strategy preserves context privacy, preventing adversaries from learning the network structure and topology. This, in turn, significantly reduces their ability to gain valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation

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

Reaction-Diffusion Processes on Interconnected Scale-Free Networks

We study the two particle annihilation reaction $A+B\rightarrow \emptyset$ on interconnected scale free networks, using different interconnecting strategies. We explore how the mixing of particles and the process evolution are influenced by the number of interconnecting links, by their functional properties, and by the interconnectivity strategies in use. We show that the reaction rates on this system are faster than what was observed in other topologies, due to the better particle mixing which suppresses the segregation effect, inline with previous studies performed on single scale free networks.Comment: 11 pages, 5 figure

Coupled effects of local movement and global interaction on contagion

By incorporating segregated spatial domain and individual-based linkage into the SIS (susceptible-infected-susceptible) model, we investigate the coupled effects of random walk and intragroup interaction on contagion. Compared with the situation where only local movement or individual-based linkage exists, the coexistence of them leads to a wider spread of infectious disease. The roles of narrowing segregated spatial domain and reducing mobility in epidemic control are checked, these two measures are found to be conducive to curbing the spread of infectious disease. Considering heterogeneous time scales between local movement and global interaction, a log-log relation between the change in the number of infected individuals and the timescale $\tau$ is found. A theoretical analysis indicates that the evolutionary dynamics in the present model is related to the encounter probability and the encounter time. A functional relation between the epidemic threshold and the ratio of shortcuts, and a functional relation between the encounter time and the timescale $\tau$ are found

Optimal curing policy for epidemic spreading over a community network with heterogeneous population

The design of an efficient curing policy, able to stem an epidemic process at an affordable cost, has to account for the structure of the population contact network supporting the contagious process. Thus, we tackle the problem of allocating recovery resources among the population, at the lowest cost possible to prevent the epidemic from persisting indefinitely in the network. Specifically, we analyze a susceptible-infected-susceptible epidemic process spreading over a weighted graph, by means of a first-order mean-field approximation. First, we describe the influence of the contact network on the dynamics of the epidemics among a heterogeneous population, that is possibly divided into communities. For the case of a community network, our investigation relies on the graph-theoretical notion of equitable partition; we show that the epidemic threshold, a key measure of the network robustness against epidemic spreading, can be determined using a lower-dimensional dynamical system. Exploiting the computation of the epidemic threshold, we determine a cost-optimal curing policy by solving a convex minimization problem, which possesses a reduced dimension in the case of a community network. Lastly, we consider a two-level optimal curing problem, for which an algorithm is designed with a polynomial time complexity in the network size.Comment: to be published on Journal of Complex Network