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PhD Defense

Learning of Networks Dynamics: Mobility, Diffusion, and Evolution


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Wednesday, December 08, 2021, 04:00pm - 06:00pm


Speaker: Haotian Wang

Location : Via Zoom


Prof. Jie Gao (advisor, chair)

Prof. Hao Wang

Prof. Peng Zhang

Prof. Feng Luo

Prof. Joseph S.B. Mitchell (external member, Stony Brook University)

Event Type: PhD Defense

Abstract: Within network science and network theory, traditional social network analysis is mainly based on static networks. Network dynamics analysis takes interactions of social features and temporal information into account, appearing to be an emergent scientific field. Different from the social networks, they consider larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of uncertainty. Due to the heterogeneity of networks, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. In this thesis, we discuss two aspects of this topic. In the first aspect, we consider the mobility properties in the social networks, i.e., human trajectories. A human trajectory is a sequence of spatial-temporal data from an individual. The data mining of human trajectories can help us improve a lot of real-world applications. However, with the increasing size of the human trajectory dataset, it brings higher challenges to our analysis. At the same time, for the human trajectory, we still lack of a comprehensive understanding of the relationship among the human trajectories, such as commonality and individuality. Thus, we focused on the statistic tools to measure the similarity between the human trajectories. Different from the traditional geometric similarity measures, such as Hausdorff distance and Frechet distance, we proposed three novel partial similarity measures, which are more suitable to the human trajectories. They can also reduce the storage requirement and save computation time. Based on these similarity measures, we designed a more advanced unsupervised clustering algorithm, integrating with the conformal prediction framework. It performs better in varied trajectory datasets compared with the classical clustering algorithms. In the second aspect, we investigate two problems in the social interactions of the social networks. For the first problem, the previous works mainly focused on the information spreading speed in the static social networks, which can be regarded as the diffusion phenomenon. However, taking mobility into the consideration, the social networks become dynamic and the interactions between different individuals happen over time. The heterogeneity of mobility plays an important role in the diffusion process. We utilize the human trajectory dataset to simulate the physical interactions among individuals to view this diffusion process. At the same time, we also investigate some targeted interventions' impact on the social networks. In the second problem, the opinion evolution process in the social networks is discussed. We proposed a co-evolution model, including the opinion dynamics and social tie dynamics, to investigate the community structure and structural balance in the final state with rigorous theoretical analysis. The simulations also are utilized to validate the communities form process.


Rutgers University School of Arts and Sciences

Contact  Jie Gao

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