A network-based realignment framework for detecting spontaneous roles in EEG hyperscanning

Social interactions are inherently asymmetric, characterized by the spontaneous emergence of roles, such as leader and follower, that dictate the flow of the exchange. While these roles are fundamental to social cognition, identifying their neural correlates remains a challenge, especially when they are not pre-assigned but emerge dynamically. This study introduces a novel framework for aligning multi-subject connectivity networks based on spontaneously emerging social roles. Central to this approach is the development of GraphMatch, a new network similarity metric designed to quantify structural correspondence across multi-subject networks. We used this metric to validate a realignment procedure driven by the Inter-Imbalance index, testing the hypothesis that individuals fulfilling similar social roles exhibit more aligned neural connectivity patterns. We applied this framework to EEG-hyperscanning data from 16 dyads performing a joint action task. Our results demonstrate that the realignment significantly enhances structural similarity across dyads, suggesting that leader-follower roles have a distinct, identifiable neural signature. Furthermore, complementary spectral validation confirmed that our algorithm captures these asymmetries, revealing a predominant directional connectivity flow from leaders to followers. By providing an approach to identify spontaneous role differentiation, this work opens new avenues for investigating asymmetric social dynamics and their possible alterations in atypical populations.