TY - JOUR
T1 - EasyGraph
T2 - A multifunctional, cross-platform, and effective library for interdisciplinary network analysis
AU - Gao, Min
AU - Li, Zheng
AU - Li, Ruichen
AU - Cui, Chenhao
AU - Chen, Xinyuan
AU - Ye, Bodian
AU - Li, Yupeng
AU - Gu, Weiwei
AU - Gong, Qingyuan
AU - Wang, Xin
AU - Chen, Yang
N1 - This work is sponsored by National Natural Science Foundation of China (no. 62072115, no. 62102094, no. 61602122, no. 61971145, and no. 62202402), Shanghai Science and Technology Innovation Action Plan Project (no. 22510713600), Guangdong Basic and Applied Basic Research Foundation (no. 2022A1515011583 and, no. 2023A1515011562), One-off Tier 2 Start-up Grant (2022/2021) of New Academics AY2020/21 of Hong Kong Baptist University, and Germany/Hong Kong Joint Research Scheme sponsored by the Research Grants Council of Hong Kong and German Academic Exchange Service of Germany (no. G-HKBU203/22).
Publisher Copyright:
© 2023 The Authors
PY - 2023/10/13
Y1 - 2023/10/13
N2 - 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.
AB - 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.
KW - interdisciplinary network analysis
KW - multiprocessing optimization
KW - hybrid Python/C++ programming
KW - structural hole theory
UR - http://www.scopus.com/inward/record.url?scp=85173221423&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2023.100839
DO - 10.1016/j.patter.2023.100839
M3 - Journal article
AN - SCOPUS:85173221423
SN - 2666-3899
VL - 4
JO - Patterns
JF - Patterns
IS - 10
M1 - 100839
ER -
