Timothy LaRock

Timothy LaRock, Chen Zhang, Jürgen Hackl

The detailed study of individual human mobility requires large-scale high-resolution datasets, but collecting such datasets in a way that is both statistically powerful and privacy preserving is a challenging and expensive task. In response, researchers have built tools to generate complex synthetic populations of agents that can be used to simulate synthetic individual mobility data, potentially obviating the difficulties of data collection. While these simulation-based approaches offer a promising avenue for expanding individual mobility research, it is difficult to asses whether such tools are effective at generating realistic mobility traces. In this work, we develop a framework for comparing observed and simulated mobility data using a higher-order network framework that focuses on analyzing patterns of movement in the paths individuals take through the underlying infrastructure network. We apply our framework to a case study comparing the NetMob 2025 Data Challenge Dataset, which includes individual mobility data for thousands of residents of the Île-de-France region, with a sophisticated open-source synthetic population and mobility simulation model of the same region. We show that while simulated mobility data is indeed promising as a surrogate for observed mobility, there are some key limitations to the simulation paradigm from a path-based perspective, which we discuss along with potential future remediations and open challenges for higher-order mobility network analysis.

Timothy LaRock, Timothy Sakharov, Sahely Bhadra et al.

Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incomplete data leads to skewed or misleading results. In this paper, we investigate limitations of learning to complete partially observed networks via node querying. Concretely, we study the following problem: given (i) a partially observed network, (ii) the ability to query nodes for their connections (e.g., by accessing an API), and (iii) a budget on the number of such queries, sequentially learn which nodes to query in order to maximally increase observability. We call this querying process Network Online Learning and present a family of algorithms called NOL*. These algorithms learn to choose which partially observed node to query next based on a parameterized model that is trained online through a process of exploration and exploitation. Extensive experiments on both synthetic and real world networks show that (i) it is possible to sequentially learn to choose which nodes are best to query in a network and (ii) some macroscopic properties of networks, such as the degree distribution and modular structure, impact the potential for learning and the optimal amount of random exploration.