SNSAlib:A python library for analyzing signed network  

作　　者：Ai-Wen Li Jun-Lin Lu Ying Fan Xiao-Ke Xu 李艾纹;陆俊霖;樊瑛;许小可

机构地区：[1]School of Systems Science,Beijing Normal University,Beijing 100875,China [2]School of Journalism and Communication,Beijing Normal University,Beijing 100875,China

出　　处：《Chinese Physics B》2025年第3期64-75,共12页中国物理B(英文版)

基　　金：Project supported by the National Natural Science Foundation of China(Grant Nos.72371031,62173065,62476045);Fundamental Research Funds for the Central Universities(Grant No.124330008)。

摘　　要：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.

关 键 词：signed networks null models topology structure statistic analysis 

分 类 号：F42[经济管理—产业经济]