EasyGraph: A multifunctional, cross-platform, and effective library for interdisciplinary network analysis

Min Gao, Zheng Li, Ruichen Li, Chenhao Cui, Xinyuan Chen, Bodian Ye, Yupeng Li, Weiwei Gu, Qingyuan Gong, Xin Wang, Yang Chen*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

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.

Original languageEnglish
Article number100839
Number of pages17
JournalPatterns
Volume4
Issue number10
DOIs
Publication statusPublished - 13 Oct 2023

Scopus Subject Areas

  • Decision Sciences(all)

User-Defined Keywords

  • interdisciplinary network analysis
  • multiprocessing optimization
  • hybrid Python/C++ programming
  • structural hole theory

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