| SPECIAL TOPIC — Computational programs in complex systems |
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SNSAlib: A python library for analyzing signed network |
| Ai-Wen Li(李艾纹)1, Jun-Lin Lu(陆俊霖)2, Ying Fan(樊瑛)1,†, and Xiao-Ke Xu(许小可)2,‡ |
1 School of Systems Science, Beijing Normal University, Beijing 100875, China;
2 School of Journalism and Communication, Beijing Normal University, Beijing 100875, China |
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Abstract The unique structure of signed networks, characterized by positive and negative edges, poses significant challenges for analyzing network topology. In recent years, various statistical algorithms have been developed to address this issue. However, there remains a lack of a unified framework to uncover the nontrivial properties inherent in signed network structures. To support developers, researchers, and practitioners in this field, we introduce a Python library named SNSAlib (Signed Network Structure Analysis), specifically designed to meet these analytical requirements. This library encompasses empirical signed network datasets, signed null model algorithms, signed statistics algorithms, and evaluation indicators. The primary objective of SNSAlib is to facilitate the systematic analysis of micro- and meso-structure features within signed networks, including node popularity, clustering, assortativity, embeddedness, and community structure by employing more accurate signed null models. Ultimately, it provides a robust paradigm for structure analysis of signed networks that enhances our understanding and application of signed networks.
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Received: 05 November 2024
Revised: 12 December 2024
Accepted manuscript online:
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PACS:
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89.75.Fb
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(Structures and organization in complex systems)
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87.23.Ge
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(Dynamics of social systems)
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05.10.-a
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(Computational methods in statistical physics and nonlinear dynamics)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 72371031, 62173065, and 62476045) and Fundamental Research Funds for the Central Universities (Grant No. 124330008). |
Corresponding Authors:
Ying Fan, Xiao-Ke Xu
E-mail: yfan@bnu.edu.cn;xuxiaoke@foxmail.com
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Cite this article:
Ai-Wen Li(李艾纹), Jun-Lin Lu(陆俊霖), Ying Fan(樊瑛), and Xiao-Ke Xu(许小可) SNSAlib: A python library for analyzing signed network 2025 Chin. Phys. B 34 038902
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