关键词: signed networks, null models, topology structure, statistic analysis

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.

Key words: signed networks, null models, topology structure, statistic analysis