RESUMEN
Networks are powerful tools for representing the relationships and interactions between entities in various disciplines. However, existing network analysis tools and packages either lack powerful functionality or are not scalable for large networks. In this descriptor, we present EasyGraph, an open-source network analysis library that supports several network data formats and powerful network mining algorithms. EasyGraph provides excellent operating efficiency through a hybrid Python/C++ implementation and multiprocessing optimization. It is applicable to various disciplines and can handle large-scale networks. We demonstrate the effectiveness and efficiency of EasyGraph by applying crucial metrics and algorithms to random and real-world networks in domains such as physics, chemistry, and biology. The results demonstrate that EasyGraph improves the network analysis efficiency for users and reduces the difficulty of conducting large-scale network analysis. Overall, it is a comprehensive and efficient open-source tool for interdisciplinary network analysis.
RESUMEN
Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better understand user behaviors of LBS, human mobility, and also temporal and spatial characteristics of people's moods.
