  Structural balance theory predicts that triads in networks gravitate towards
stable configurations. The theory has been verified for undirected graphs.
Since real-world networks are often directed, we introduce a novel method for
considering both transitivity and sign consistency for evaluating partial
balance in signed digraphs. We test our approach on graphs constructed by using
different methods for identifying edge signs: natural language processing to
infer signs from underlying text data, and self-reported survey data. Our
results show that for various social contexts and edge sign detection methods,
partial balance of these digraphs are moderately high, ranging from 61% to 96%.
Our approach not only enhances the theoretical framework of structural balance
but also provides practical insights into the stability of social networks,
enabling a deeper understanding of interpersonal and group dynamics across
different communication platforms.
