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

从网络科学的角度探索机器介导的社交互动网格

• 批准号: RGPIN-2017-05201
• 负责人: Chen, Yuanzhu
• 金额: $ 3.79万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749787
• 关键词: Exploring; Machine; Mediated; Social; Interaction

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.

拟议的研究计划调查了日益流行的移动设备和智能设备促进的社交网络的特性，这些设备和智能设备能够通过基础设施进行数据通信，并在它们之间直接通信。其目的是提供对这种多模式、时变、复杂网络的健壮性和数据承载能力的理解。这项研究提出的解决方案将成为信息技术部门和其他学科之间的中介，如公共卫生和社会网络研究，以进行合作研究。现代移动设备配备了大量传感器和无线电，并能够以各种模式传递传感数据和用户生成的数据。当它们在服务区时，比特通过Wi-Fi或蜂窝网络传输。最近，智能移动设备的潜力被全新的通信方法进一步释放，包括移动自组织网络(MANET)和延迟容忍网络(DTN)。当数据生产者和消费者不在直接范围内时，MANET协调中间网络节点转发数据分组，而DTN进一步利用这些节点的移动性来机械地跨网络的不同部分传送数据。这种创新的网络方案有效地扩大了设备在空间和时间上的通信范围，以接触到更多的潜在同行。在形式上，包含空间和时间域的网络被建模为时空图。一方面，用户将享受这样一个事实，即他们的消息可以通过这一高度丰富的智能设备网络传输更远的距离。另一方面，科学研究界必须了解网络在数据传输和影响传播方面的能力，以及对故障和攻击的恢复能力。在拟议的研究中，我将首先在智能移动设备上设计一个消息记录工具，它使用机上的短程无线电形成一个无基础设施的无线网络。该工具不仅将记录移动同行的遭遇，还将在用户移动时通过断断续续的无线电链路传递跟踪消息。然后，在设备上纵向收集的数据将与同一时期的社交网络数据相结合，以重建时空网络。为了分析这样一个复杂的网络，网络科学的结果将被扩展，以适应多种链路类型，并描绘网络随时间的演变。通过网络结构，我们努力了解它的行为，使用分析工具，如站点渗透，疾病传播的流行病模型，以及一般的动力系统。所获得的知识将帮助我们在加拿大偏远的农村和北极地区提供创新和可靠的信息共享，并在全国范围内建立有弹性的网络系统。