Software:EasyGraph
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| Developer(s) | Min Gao, Zhen Li, Ruichen Li, Chenhao Cui, Xinyuan Chen, Bodian Ye, jiawei Li, Haoran Qin, Xinlei He, Yi Sun, Yuting Shao, Zihang Lin, Yang Chen, Qingyuan Gong |
|---|---|
| Initial release | 7 August 2023[1] |
| Written in | Python, C++ |
| Operating system | Linux, Windows, macOS |
| Size | 3.2 MB |
| Available in | English |
| Type | Programming |
| License | BSD-3-Clause |
| Website | easy-graph |
EasyGraph[2][3] is an open-source network analysis and network embedding[4] software package. It is mainly written in Python and supports analysis for undirected networks and directed networks. EasyGraph supports various formats of network data and covers a series of important network analysis algorithms for detecting community structure, structural hole spanner[5] detection. Moreover, EasyGraph implements some key elements using C++ and introduces multiprocessing optimization to achieve better efficiency.
History
EasyGraph was developed by the DataNET group at Fudan University. Our goal is to build a cross-platform library which could be useful for interdisciplinary network analytics.
It's first version 1.0 has been launched in 2023.
Applications
EasyGraph has multiple notable applications including basic properties and operation of networks, detection of structural hole spanners, network embedding, network construction, and community detection.
See also
- Graph (discrete mathematics)
- Graph drawing
- Graph theory
- Graph (data structure)
- Social network analysis software
- File formats
- Dot language
- GraphML
- Graph Modelling Language
- Related software
References
- ↑ https://github.com/easy-graph/Easy-Graph EasyGraph version 1.0 release date
- ↑ Min Gao and Zheng Li and Ruichen Li and Chenhao Cui and Xinyuan Chen and Bodian Ye and Yupeng Li and Weiwei Gu and Qingyuan Gong and Xin Wang and Yang Chen (2023). "EasyGraph: A Multifunctional, Cross-Platform, and Effective Library for Interdisciplinary Network Analysis". Patterns 4 (10).
- ↑ EasyGraph (2023-10-13). EasyGraph Tutorials. YouTube.
- ↑ Grover, Aditya; Leskovec, Jure (2016). "Node2vec". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. pp. 855–864. doi:10.1145/2939672.2939754. ISBN 9781450342322. Bibcode: 2016arXiv160700653G.
- ↑ Burt, R. (2004). "Structural holes and good ideas". American Journal of Sociology 110 (2): 349–399. doi:10.1086/421787.
- ↑ "Pajek / PajekXXL / Pajek3XL". http://mrvar.fdv.uni-lj.si/pajek/.
External links
Category:Network theory Category:Free data analysis software
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