Mesoscopic structure and social aspects of human mobility

The individual movements of large numbers of people are important in many contexts, from urban planning to disease spreading. Datasets that capture human mobility are now available and many interesting features have been discovered, including the ultra-slow spatial growth of individual mobility. However, the detailed substructures and spatiotemporal flows of mobility - the sets and sequences of visited locations - have not been well studied. We show that individual mobility is dominated by small groups of frequently visited, dynamically close locations, forming primary "habitats" capturing typical daily activity, along with subsidiary habitats representing additional travel. These habitats do not correspond to typical contexts such as home or work. The temporal evolution of mobility within habitats, which constitutes most motion, is universal across habitats and exhibits scaling patterns both distinct from all previous observations and unpredicted by current models. The delay to enter subsidiary habitats is a primary factor in the spatiotemporal growth of human travel. Interestingly, habitats correlate with non-mobility dynamics such as communication activity, implying that habitats may influence processes such as information spreading and revealing new connections between human mobility and social networks.Comment: 7 pages, 5 figures (main text); 11 pages, 9 figures, 1 table (supporting information

Multichannel social signatures and persistent features of ego networks

The structure of egocentric networks reflects the way people balance their need for strong, emotionally intense relationships and a diversity of weaker ties. Egocentric network structure can be quantified with ’social signatures’, which describe how people distribute their communication effort across the members (alters) of their personal networks. Social signatures based on call data have indicated that people mostly communicate with a few close alters; they also have persistent, distinct signatures. To examine if these results hold for other channels of communication, here we compare social signatures built from call and text message data, and develop a way of constructing mixed social signatures using both channels. We observe that all types of signatures display persistent individual differences that remain stable despite the turnover in individual alters. We also show that call, text, and mixed signatures resemble one another both at the population level and at the level of individuals. The consistency of social signatures across individuals for different channels of communication is surprising because the choice of channel appears to be alter-specific with no clear overall pattern, and ego networks constructed from calls and texts overlap only partially in terms of alters. These results demonstrate individuals vary in how they allocate their communication effort across their personal networks and this variation is persistent over time and across different channels of communication

Exploring the Evolution of Node Neighborhoods in Dynamic Networks

Dynamic Networks are a popular way of modeling and studying the behavior of evolving systems. However, their analysis constitutes a relatively recent subfield of Network Science, and the number of available tools is consequently much smaller than for static networks. In this work, we propose a method specifically designed to take advantage of the longitudinal nature of dynamic networks. It characterizes each individual node by studying the evolution of its direct neighborhood, based on the assumption that the way this neighborhood changes reflects the role and position of the node in the whole network. For this purpose, we define the concept of \textit{neighborhood event}, which corresponds to the various transformations such groups of nodes can undergo, and describe an algorithm for detecting such events. We demonstrate the interest of our method on three real-world networks: DBLP, LastFM and Enron. We apply frequent pattern mining to extract meaningful information from temporal sequences of neighborhood events. This results in the identification of behavioral trends emerging in the whole network, as well as the individual characterization of specific nodes. We also perform a cluster analysis, which reveals that, in all three networks, one can distinguish two types of nodes exhibiting different behaviors: a very small group of active nodes, whose neighborhood undergo diverse and frequent events, and a very large group of stable nodes

Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals. This ability can be associated with spatial reasoning, where an agent is able to perceive spatial relationships and regularities, and discover object characteristics. Recent work introduces learnable policies parametrized by deep neural networks and trained with Reinforcement Learning (RL). In classical RL setups, the capacity to map and reason spatially is learned end-to-end, from reward alone. In this setting, we introduce supplementary supervision in the form of auxiliary tasks designed to favor the emergence of spatial perception capabilities in agents trained for a goal-reaching downstream objective. We show that learning to estimate metrics quantifying the spatial relationships between an agent at a given location and a goal to reach has a high positive impact in Multi-Object Navigation settings. Our method significantly improves the performance of different baseline agents, that either build an explicit or implicit representation of the environment, even matching the performance of incomparable oracle agents taking ground-truth maps as input. A learning-based agent from the literature trained with the proposed auxiliary losses was the winning entry to the Multi-Object Navigation Challenge, part of the CVPR 2021 Embodied AI Workshop

Modular wireless networks for infrastructure-challenged environments

While access to Internet and cellular connectivity is easily achieved in densely-populated areas, provisioning of communication services is much more challenging in remote rural areas. At the same time Internet access is of critical importance to residents of such rural communities. People's curiosity and realization of the opportunities provided by Internet and cellular access is the key ingredient to adoption. However, poor network performance can easily impede the process of adoption by discouraging people to access and use connectivity. With this in mind, we evaluate performance and adoption of various connectivity technologies in rural developing regions and identify avenues that need immediate attention to guarantee smoother technology adoption. In light of this analysis we propose novel system designs that meet these needs. In this thesis we focus on cellular and broadband Internet connectivity. Commercial cellular networks are highly centralized, which requires costly backhaul. This, coupled with high price for equipment, maintenance and licensing renders cellular network access commercially-infeasible in rural areas. At the same time rural cellular communications are highly local: 70% of the rural-residential calls have an originator-destination pair within the same antenna. In line with this observation we design a low-cost cellular network architecture dubbed Kwiizya, to provide local voice and text messaging services in a rural community. Where outbound connectivity is available, Kwiizya can provide global services. While commercial networks are becoming more available in rural areas they are often out of financial reach of rural residents. Furthermore, these networks typically provide only basic voice and SMS services and no mobile data. To address these challenges, our proposed work allows Kwiizya to operate in coexistence with commercial cellular networks in order to extend local coverage and provide more advanced services that are not delivered by the commercial networks. Internet connectivity in rural areas is typically provided through slow satellite links. The challenges in performance and adoption of such networks have been previously studied. We add a unique dataset and consequent analysis to this spectrum of work, which captures the upgrade of the gateway connectivity in the rural community of Macha, Zambia from a 256kbps satellite link to a more capable 2Mbps terrestrial link. We show that the improvement in performance and user experience is not necessarily proportional to the bandwidth increase. While this increase improved the network usability, it also opened opportunities for adoption of more demanding services that were previously out of reach. As a result the network performance was severely degraded over the long term. To address these challenges we employ white space communication both for connectivity to more capable remote gateways, as well as for end user connectivity. We develop VillageLink, a distributed method that optimizes channel allocation to maximize throughput and enables both remote gateway access as well as end user coverage. While VillageLink features lightweight channel probing, we also consider external sources of channel availability. We design a novel approach for estimation of channel occupancy called TxMiner, which is capable of extracting transmitter characteristics from raw spectrum measurements. We study the adoption and implications of network connectivity in rural communities. In line with the results of our analyses we design and build system architectures that are geared to meet critical needs in these communities. While the focus of analysis in this thesis is on rural sub-Saharan Africa, the proposed designs and system implementations are more general and can serve in infrastructure-challenged communities across the world