Exploring Machine-Mediated Social Interaction Mesh from a Network Science Perspective

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The proposed research program investigates properties of the mesh of social interaction facilitated by the increasingly prevalent mobile devices and smart gadgets, which are capable of data communication through infrastructure and directly between themselves. The objective is to provide an understanding of the robustness and data-carrying capacity of such a multi-modal, time-varying, complex network. The solutions produced in this research will constitute an intermediary between the IT sector and other disciplines, such as public health and social network studies, for collaborative research. ***Modern mobile devices are equipped with a multitude of sensors and radios, and are capable of communicating sensed and user-generated data in a variety of modes. When they are in serviced areas, bits are transported by Wi-Fi or cellular networks. Recently, the potential of smart mobile devices is further unleashed by radically new communication methods, including mobile ad hoc networking (MANET) and delay-tolerant networking (DTN). MANET coordinates intermediate network nodes in forwarding data packets when the data producer and consumer are not directly within range, while DTN further capitalizes on the mobility of these nodes to mechanically carry data across different sections of the network. Such innovative networking schemes effectively extend the devices' communication range over space and time to reach many more potential peers. Formally, the network encompassing the space and time domains is modeled as spatial-temporal graph. On one hand, users will enjoy the fact that their messages can travel a greater distance through this highly enriched mesh of smart devices. On the other hand, it is imperative for the scientific research community to understand the network's capacity in data transportation and influence spreading, and its resilience to failures and attacks.***In the proposed research, I will first devise a message logging tool on smart mobile devices which form an infrastructureless wireless network using the short-range radios onboard. The tool will not only log encounters of the mobile peers, but will also pass tracing messages over the intermittent radio links as users move around. The data gathered on devices longitudinally will then be coupled with social network data during the same period to reconstruct the spatial-temporal network. To analyze such a complex network, results in network science will be extended to accommodate a multitude of link types and to portray the network's evolution over time. Through the network structure, we strive to understand its behaviour using analytical tools such as site percolation, epidemic model of disease spread, and dynamical systems generally. The acquired knowledge will help us provide innovative and reliable information sharing in the remote rural and arctic areas of Canada, and build resilient cyber systems nation-wide.