A0201
Title: Nonparametric inference for balance in signed networks
Authors: Weijing Tang - Carnegie Mellon University (United States) [presenting]
Abstract: In many real-world networks, relationships often go beyond simple dyadic presence or absence; they can be positive, like friendship, alliance, and mutualism, or negative, characterized by enmity, disputes, and competition. To understand the formation mechanism of such signed networks, the social balance theory sheds light on the dynamics of positive and negative connections. In particular, it characterizes the proverbs, "a friend of my friend is my friend" and "an enemy of my enemy is my friend". A nonparametric inference approach is proposed for assessing empirical evidence for the balance theory in real-world signed networks. The generating process of signed networks is first characterized with node exchangeability, and a nonparametric sparse graphon model is proposed. Under this model, confidence intervals are constructed for the population parameters associated with balance theory and establish their theoretical validity. The inference procedure offers higher-order accuracy and is more computationally efficient than bootstrap-based methods. By applying the method, strong real-world evidence for balance theory in signed networks across various domains is found, extending its applicability beyond social psychology.
