Network-wide shocking events through the lens of node representation shift

In the context of online social networks, network-wide shocking events, i.e. events widely recognized by most network users, are worthy of attention since they significantly impact users’ behavior and interactions and have the potential to disrupt the distribution of temporal data. However, how users behave before, during, and after such events is still not clear. To address this gap we rely on the framework of graph representation learning, particularly focusing on Temporal Graph Neural Networks (TGNNs). In particular, we investigate the dynamics of node representations returned by TGNNs during a network-wide shocking event and examine how shifts in node representation can mirror the effects of such events on users’ habits. We utilize a dataset from Steemit, a blockchain-based online social network, which experienced a network-wide shocking event, i.e. an important user migration caused by a hard fork in the supporting blockchain. Our findings highlight that node representations are influenced by the occurrence of the shocking event. We observe shifts in node representations, indicating changes in individual users’ behavior during the event.