Balance theory explains the forces behind the structure of social systems, which are commonly modeled as static undirected signed networks. We expand this modeling approach to incorporate directionality of edges, and consider three levels of analysis: triads, subgroups, and the whole network. For triad-level balance, we operationalize a new measure by utilizing semicycles that satisfy the condition of transitivity. For subgroup-level balance, we propose measures of cohesiveness (intra-group solidarity) and divisiveness (inter-group antagonism) to capture balance within and among subgroups of the network using the most fitting partition of nodes into two groups. For network-level balance, we re-purpose the normalized line index to incorporate directionality, and provide the proportion of edges whose position suits balance. Through extensive computational analysis, we quantify and analyze patterns of social structure in triads, subgroups, and the whole network across a range of social settings from college students and Wikipedia editors to philosophers and Bitcoin traders. We then apply our multilevel framework of analysis to examine balance in temporal and multilayer networks, which demonstrates the generalizability of our approach to evaluating balance, and leads to new observations on balance with respect to time and layer dimensions. Our complementary findings on a variety of social networks highlight the need to evaluate balance at different levels. We propose a comprehensive yet parsimonious approach to address this need.
SDGNN: Learning Node Representation for Signed Directed Networks
CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所网络数据科学与技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
On the Effects of Decentralized Moderation on Network Robustness and Information Diffusion in Mastodon
Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；Department of Informatics, Modeling, Electronics and System Engineering University of Calabria（卡塔尼亚大学信息、建模、电子与系统工程系）；Department of Computing, Northeastern University London（伦敦东北大学计算机系）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), CSIC-UIB（跨学科物理与复杂系统研究所 (IFISC)，西班牙国家研究委员会-巴利阿里群岛大学）；“Enrico Fermi” Research Center (CREF)（“恩里科·费米”研究中心 (CREF)）
