DOI: 10.57760/sciencedb.j00113.00178
title: The data and code of the article ''SNSAlib: a python library for analyzing signed network''
title: The data and code of the article ''SNSAlib: a python library for analyzing signed network''

description: The data and code related to the article ''SNSAlib: a python library for analyzing signed network'' was published in the journal of Chinese Physics B. This project contains null model construction of signed networks and its statistic features. The whole project is divided into three parts, as follows: Part1: signed networks datasetsThis part involves ten empirical signed network datasets: SPP, GGS, Wiring, Sampson, Teams, Alpha, OTC, Wiki, Slashdot, and Epinions. The first five datasets are sourced from offline real-world social networks, and the latter five are obtained from online internet platforms. The processed data is stored as a triplet in a text file (.txt). Part2: null model construction of signed networksThis part is null model construction of undirected signed networks. It have seven different methods of null model construction of undirected signed networks: positive-edge randomized null model, negative-edge randomized null model, the positive-edge and negative-edge randomized null model, full-edge randomized null model, signed randomized null model, diminish community structure null model, and enhance community structure null model. Part3: statistic features of signed networksThis part is statistic features of signed model, which can describe the difference between the null model and the real networks, and discover the extraordinary characteristics of real networks. These statistic features are common neighbors, matching coefficient, excess average degree, clustering coefficient, embeddedness, FMF, FECS and DECDS.    
description: The data and code related to the article ''SNSAlib: a python library for analyzing signed network'' was published in the journal of Chinese Physics B. This project contains null model construction of signed networks and its statistic features. The whole project is divided into three parts, as follows: Part1: signed networks datasetsThis part involves ten empirical signed network datasets: SPP, GGS, Wiring, Sampson, Teams, Alpha, OTC, Wiki, Slashdot, and Epinions. The first five datasets are sourced from offline real-world social networks, and the latter five are obtained from online internet platforms. The processed data is stored as a triplet in a text file (.txt). Part2: null model construction of signed networksThis part is null model construction of undirected signed networks. It have seven different methods of null model construction of undirected signed networks: positive-edge randomized null model, negative-edge randomized null model, the positive-edge and negative-edge randomized null model, full-edge randomized null model, signed randomized null model, diminish community structure null model, and enhance community structure null model. Part3: statistic features of signed networksThis part is statistic features of signed model, which can describe the difference between the null model and the real networks, and discover the extraordinary characteristics of real networks. These statistic features are common neighbors, matching coefficient, excess average degree, clustering coefficient, embeddedness, FMF, FECS and DECDS.