Temporal networks of face-to-face human interactions

The ever increasing adoption of mobile technologies and ubiquitous services allows to sense human behavior at unprecedented levels of details and scale. Wearable sensors are opening up a new window on human mobility and proximity at the finest resolution of face-to-face proximity. As a consequence, empirical data describing social and behavioral networks are acquiring a longitudinal dimension that brings forth new challenges for analysis and modeling. Here we review recent work on the representation and analysis of temporal networks of face-to-face human proximity, based on large-scale datasets collected in the context of the SocioPatterns collaboration. We show that the raw behavioral data can be studied at various levels of coarse-graining, which turn out to be complementary to one another, with each level exposing different features of the underlying system. We briefly review a generative model of temporal contact networks that reproduces some statistical observables. Then, we shift our focus from surface statistical features to dynamical processes on empirical temporal networks. We discuss how simple dynamical processes can be used as probes to expose important features of the interaction patterns, such as burstiness and causal constraints. We show that simulating dynamical processes on empirical temporal networks can unveil differences between datasets that would otherwise look statistically similar. Moreover, we argue that, due to the temporal heterogeneity of human dynamics, in order to investigate the temporal properties of spreading processes it may be necessary to abandon the notion of wall-clock time in favour of an intrinsic notion of time for each individual node, defined in terms of its activity level. We conclude highlighting several open research questions raised by the nature of the data at hand.Comment: Chapter of the book "Temporal Networks", Springer, 2013. Series: Understanding Complex Systems. Holme, Petter; Saram\"aki, Jari (Eds.

Three applications for mobile epidemic algorithms

This paper presents a framework for the pervasive sharing of data using wireless networks. 'FarCry' uses the mobility of users to carry files between separated networks. Through a mix of ad-hoc and infrastructure-based wireless networking, files are transferred between users without their direct involvement. As users move to different locations, files are then transmitted on to other users, spreading and sharing information. We examine three applications of this framework. Each of these exploits the physically proximate nature of social gatherings. As people group together in, for example, business meetings and cafés, this can be taken as an indication of similar interests, e.g. in the same presentation or in a type of music. MediaNet affords sharing of media files between strangers or friends, MeetingNet shares business documents in meetings, and NewsNet shares RSS feeds between mobile users. NewsNet also develops the use of pre-emptive caching: collecting information from others not for oneself, but for the predicted later sharing with others. We offer observations on developing this system for a mobile, multi-user, multi-device environment

On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

Agent‐based modeling of malware dynamics in heterogeneous environments

The increasing convergence of power‐law networks such as social networking and peer‐to‐peer applications, web‐delivered applications, and mobile platforms makes today's users highly vulnerable to entirely new generations of malware that exploit vulnerabilities in web applications and mobile platforms for new infections, while using the power‐law connectivity for finding new victims. The traditional epidemic models based on assumptions of homogeneity, average‐degree distributions, and perfect‐mixing are inadequate to model this type of malware propagation. In this paper, we study four aspects crucial to modeling malware propagation: application‐level interactions among users of such networks , local network structure , user mobility , and network coordination of malware such as botnets . Since closed‐form solutions of malware propagation considering these aspects are difficult to obtain, we describe an open‐source, flexible agent‐based emulation framework that can be used by malware researchers for studying today's complex malware. The framework, called Agent‐Based Malware Modeling (AMM), allows different applications, network structure, network coordination, and user mobility in either a geographic or a logical domain to study various infection and propagation scenarios. In addition to traditional worms and viruses, the framework also allows modeling network coordination of malware such as botnets. The majority of the parameters used in the framework can be derived from real‐life network traces collected from a network, and therefore, represent realistic malware propagation and infection scenarios. As representative examples, we examine two well‐known malware spreading mechanisms: (i) a malicious virus such as Cabir spreading among the subscribers of a cellular network using Bluetooth and (ii) a hybrid worm that exploit email and file‐sharing to infect users of a social network. In both cases, we identify the parameters most important to the spread of the epidemic based upon our extensive simulation results. Copyright © 2011 John Wiley & Sons, Ltd. This paper presents a novel agent‐based framework for realistic modeling of malware propagation in heterogeneous networks, applications and platforms. The majority of the parameters used in the framework can be derived from real‐life network traces collected from a network, and therefore, represent realistic malware propagation and infection scenarios for the given network. Two well‐known malware spreading mechanisms in traditional as well as mobile environments were studied using extensive simulations within the framework and the most important spreading parameters were identified.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101832/1/sec298.pd

Immunization strategies for epidemic processes in time-varying contact networks

Spreading processes represent a very efficient tool to investigate the structural properties of networks and the relative importance of their constituents, and have been widely used to this aim in static networks. Here we consider simple disease spreading processes on empirical time-varying networks of contacts between individuals, and compare the effect of several immunization strategies on these processes. An immunization strategy is defined as the choice of a set of nodes (individuals) who cannot catch nor transmit the disease. This choice is performed according to a certain ranking of the nodes of the contact network. We consider various ranking strategies, focusing in particular on the role of the training window during which the nodes' properties are measured in the time-varying network: longer training windows correspond to a larger amount of information collected and could be expected to result in better performances of the immunization strategies. We find instead an unexpected saturation in the efficiency of strategies based on nodes' characteristics when the length of the training window is increased, showing that a limited amount of information on the contact patterns is sufficient to design efficient immunization strategies. This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes. We also observe that the efficiency of strategies that include an element of randomness and are based on temporally local information do not perform as well but are largely independent on the amount of information available

A Monte Carlo method for the spread of mobile malware

A new model for the spread of mobile malware based on proximity (i.e. Bluetooth, ad-hoc WiFi or NFC) is introduced. The spread of malware is analyzed using a Monte Carlo method and the results of the simulation are compared with those from mean field theory.Comment: 11 pages, 2 figure

Epidemic Spreading with External Agents

We study epidemic spreading processes in large networks, when the spread is assisted by a small number of external agents: infection sources with bounded spreading power, but whose movement is unrestricted vis-\`a-vis the underlying network topology. For networks which are `spatially constrained', we show that the spread of infection can be significantly speeded up even by a few such external agents infecting randomly. Moreover, for general networks, we derive upper-bounds on the order of the spreading time achieved by certain simple (random/greedy) external-spreading policies. Conversely, for certain common classes of networks such as line graphs, grids and random geometric graphs, we also derive lower bounds on the order of the spreading time over all (potentially network-state aware and adversarial) external-spreading policies; these adversarial lower bounds match (up to logarithmic factors) the spreading time achieved by an external agent with a random spreading policy. This demonstrates that random, state-oblivious infection-spreading by an external agent is in fact order-wise optimal for spreading in such spatially constrained networks