Analyzing temporal scale behaviour of connectivity properties of node encounters

Nowadays the growing popularity of wireless networks, combined with a wide availability of personal wireless devices, make the role of human mobility modeling more prominent in wireless networks, particularly in infrastructure-less networks such as Delay Tolerant Networks and Opportunistic Networks. The knowledge about encounters’ patterns among mobile nodes will be helpful for understanding the role and potential of mobile devices as relaying nodes. Data about the usage of Wi-Fi networks can be exploited to analyze the patterns of encounters between pairs of mobile devices and then be extrapolated for other contexts. Since human mobility occurs in different spatial and temporal scales, the role of scale in mobility modeling is crucial. Although spatial properties of mobility have been studied in different scales, by our knowledge there is no fundamental perspective about human mobility properties at different temporal scales. In this paper we evaluate the connectivity properties of node encounters at different temporal durations. We observed that connectivity properties of node encounters follow almost the same trends in different time intervals, although slopes and exponential decaying rates may be different. Our observations illustrate that networks formed from encounters of nodes extracted from Wi-Fi traces do not exhibit a scale free behaviour.Fundação para a Ciência e a Tecnologi

Cluster Aware Mobility Encounter Dataset Enlargement

The recent emerging fields in data processing and manipulation has facilitated the need for synthetic data generation. This is also valid for mobility encounter dataset generation. Synthetic data generation might be useful to run research-based simulations and also create mobility encounter models. Our approach in this paper is to generate a larger dataset by using a given dataset which includes the clusters of people. Based on the cluster information, we created a framework. Using this framework, we can generate a similar dataset that is statistically similar to the input dataset. We have compared the statistical results of our approach with the real dataset and an encounter mobility model generation technique in the literature. The results showed that the created datasets have similar statistical structure with the given dataset.Comment: 5 pages, 4 figures. In 2019 International Wireless Communications and Mobile Computing Conference (IWCMC), June 201

Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

Physics of epidemics on contact networks with spatial and temporal features

openThe outbreak of an infectious disease transmitted with close contacts does not only depend on the characteristic of the infection, but also on human-to-human contact behavior. This aspect is difficult to capture in physics model of epidemics because human behavior presents complex patterns: heterogeneities, recurrence, and spatial and temporal correlations. Key statistical features of human contact patterns are being uncovered by the increasing efforts to collect contact data in selected cohorts and analyze them. The theory of temporal networks provides a convenient framework to model the measured contact patterns. The aim of this work is to build a generative model of the temporal network of human contacts, where the features of interest can be turned on and off to inspect their relevance in the dynamics of epidemics. In particular, we address how the network features impact the phase transition between epidemic extinction and invasion, the epidemic dynamics at the early stage and the epidemic final outcome.The outbreak of an infectious disease transmitted with close contacts does not only depend on the characteristic of the infection, but also on human-to-human contact behavior. This aspect is difficult to capture in physics model of epidemics because human behavior presents complex patterns: heterogeneities, recurrence, and spatial and temporal correlations. Key statistical features of human contact patterns are being uncovered by the increasing efforts to collect contact data in selected cohorts and analyze them. The theory of temporal networks provides a convenient framework to model the measured contact patterns. The aim of this work is to build a generative model of the temporal network of human contacts, where the features of interest can be turned on and off to inspect their relevance in the dynamics of epidemics. In particular, we address how the network features impact the phase transition between epidemic extinction and invasion, the epidemic dynamics at the early stage and the epidemic final outcome

There and back again: detecting regularity in human encounter communities

Detecting communities that recur over time is a challenging problem due to the potential sparsity of encounter events at an individual scale and inherent uncertainty in human behavior. Existing methods for community detection in mobile human encounter networks ignore the presence of temporal patterns that lead to periodic components in the network. Daily and weekly routine are prevalent in human behavior and can serve as rich context for applications that rely on person-to-person encounters, such as mobile routing protocols and intelligent digital personal assistants. In this article, we present the design, implementation, and evaluation of an approach to decentralized periodic community detection that is robust to uncertainty and computationally efficient. This alternative approach has a novel periodicity detection method inspired by a neural synchrony measure used in the field of neurophysiology. We evaluate our approach and investigate human periodic encounter patterns using empirical datasets of inferred and direct-sensed encounters

Complex networks analysis in socioeconomic models

This chapter aims at reviewing complex networks models and methods that were either developed for or applied to socioeconomic issues, and pertinent to the theme of New Economic Geography. After an introduction to the foundations of the field of complex networks, the present summary adds insights on the statistical mechanical approach, and on the most relevant computational aspects for the treatment of these systems. As the most frequently used model for interacting agent-based systems, a brief description of the statistical mechanics of the classical Ising model on regular lattices, together with recent extensions of the same model on small-world Watts-Strogatz and scale-free Albert-Barabasi complex networks is included. Other sections of the chapter are devoted to applications of complex networks to economics, finance, spreading of innovations, and regional trade and developments. The chapter also reviews results involving applications of complex networks to other relevant socioeconomic issues, including results for opinion and citation networks. Finally, some avenues for future research are introduced before summarizing the main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared for Complexity and Geographical Economics - Topics and Tools, P. Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published

Memory effects induce structure in social networks with activity-driven agents

Activity-driven modeling has been recently proposed as an alternative growth mechanism for time varying networks, displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections, thus leading to random networks that fail to reproduce two-nodes degree correlations and the high clustering coefficient widely observed in real social networks. In this work we introduce these missing topological features by accounting for memory effects on the dynamic evolution of time-aggregated networks. To this end, we propose an activity-driven network growth model including a triadic-closure step as main connectivity mechanism. We show that this mechanism provides some of the fundamental topological features expected for social networks. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present two cases of study, one comprising face-to-face encounters in a closed gathering, while the other one from an online social friendship network.Comment: 19 pages, 12 figures, Major changes. Re-written wor