# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 1 Introduction  
\"Where people are\" is one of the basic questions asked by researchers and practitioners in many applications, like urban planning, infrastructure management, and public health and safety [1, 2, 3]. With the emergence of location-aware technologies and the widespread availability of portable devices, big spatio-temporal data on human mobility, especially in urban areas, have been collected based using various instruments [4]. Data-driven approaches in mobility research have been intensively employed by academic communities, with expanding topics including moving-object data acquisition, trajectory data management and processing, trajectory analytics and clustering, and deep learning for trajectory computing [5, 6]. Fundamental studies by [PERSON] et al. (2008) using mobile phonedata revealed universal patterns in human mobility [7], while [PERSON] et al. quantified the theoretical limits of mobility predictability through entropy analysis [8]. Researchers from many fields have been working to build an integrated science of movement and further a comprehensive understanding of human mobility and related spatial dynamics [9]. Human clustering techniques have been extended to various domains. In urban planning, clustering algorithms help in understanding pedestrian movement patterns to optimize city layouts and improve public safety [10]. [PERSON]'s social physics framework (2014) demonstrates how collective mobility patterns emerge from individual interactions [11], providing theoretical support for these applications. In marketing, the clustering of consumer behavior based on shopping patterns and visited locations enables businesses to target specific customer groups more effectively [12]. Recent work by [PERSON] et al. (2016) on population-weighted network efficiency provides methodological references for analyzing commercial behavior patterns [13]. Furthermore, analyzing patient trajectories within hospital premises can enhance the management of healthcare services and improve patient care in healthcare [14]. Among trajectory data analysis and knowledge discovery methods, trajectory clustering can help to reveal underlying spatial patterns of individual and group movements across time and space, which can further be used to conduct human activity prediction and movement simulation [5; 6; 8; 9]. In these scenarios, trajectories are taken as proxies for locations of human individuals. Spatio-temporal characteristics of one trajectory and similarities among trajectories are used to represent human behaviors and activities, as well as for human clustering and prediction, which further generate possibilities to reconstruct spatial dynamics of social communities [2; 9; 12]. In particular, recent studies have increasingly employed mobile phone call detail records (CDRs) to analyze human trajectories, presenting valuable insights into movement patterns, social interactions, and urban mobility at large scales [15]. [PERSON] et al. (2015) demonstrated that mobile phone data, beyond basic mobility analysis, can be leveraged to infer an individual's socioeconomic status and social behavior [16]. Human groups can be identified further by clustering algorithms to contribute to informed urban planning [17]. Taxi trajectories generated based on global navigation satellite systems (GNSSs) are another representative instrument for understanding urban dynamics by clustering vehicle behavior and transportation demands [18]. Recent studies have mainly focused on the similarities of geometric measurements and temporal attributes of trajectories [7]. Various algorithms, such as the edit distance on real sequence, longest common subsequence (LCS), and dynamic time warping (DTW) algorithms, have been implemented to calculate similarities among trajectories. Clustering algorithms have also been used to categorize individuals into typical groups [15; 19].