Gao Min, Li Zheng, Li Ruichen, Cui Chenhao, Chen Xinyuan, Ye Bodian, Li Yupeng, Gu Weiwei, Gong Qingyuan, Wang Xin, Chen Yang
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China.
Department of Interactive Media, Hong Kong Baptist University, Hong Kong, China.
Patterns (N Y). 2023 Sep 5;4(10):100839. doi: 10.1016/j.patter.2023.100839. eCollection 2023 Oct 13.
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.
网络是表示各学科中实体之间关系和相互作用的强大工具。然而，现有的网络分析工具和软件包要么缺乏强大的功能，要么对于大型网络不可扩展。在本描述中，我们介绍了EasyGraph，一个支持多种网络数据格式和强大网络挖掘算法的开源网络分析库。EasyGraph通过混合Python/C++实现和多进程优化提供了出色的运行效率。它适用于各个学科，并且可以处理大规模网络。我们通过将关键指标和算法应用于物理、化学和生物学等领域的随机网络和真实网络，展示了EasyGraph的有效性和效率。结果表明，EasyGraph提高了用户的网络分析效率，降低了进行大规模网络分析的难度。总体而言，它是一个用于跨学科网络分析的全面且高效的开源工具。
