Precise identification of seizure propagation pathways is a critical prerequisite for targeted interventions in epilepsy, such as responsive neurostimulation. However, given the highly dynamic nature of epileptic networks, traditional static or purely probabilistic connectivity measures often fail to capture the continuous temporal flow of seizure activity. To address this challenge, we introduce the Continuous Temporal Graph (CTG) framework, a novel mathematical approach designed to model the temporal evolution of seizure pathways directly from intracranial EEG (iEEG/sEEG) recordings. Unlike discrete-time methods that compress or lose fuzzy temporal information, the CTG framework represents functional interactions not as static weights, but as continuous sets of active time intervals. By employing interval algebra—specifically union (\oplus) and sequential composition (\otimes)—we strictly capture the temporal continuity of seizure spread. Within this framework, we propose a novel metric, T_{bridge}, which utilizes counterfactual reasoning to quantify the temporal dependency of propagation on specific functional connections. Rather than asserting the presence of permanent structural defects, T_{bridge} precisely identifies pathways that act as indispensable functional bridges at specific, critical moments during a seizure. Evaluated on both simulated datasets and patient iEEG recordings, the proposed framework successfully isolates time-dependent, non-redundant critical pathways that traditional statistical metrics may obscure. Ultimately, this study provides a new mathematical lens for observing temporal network dynamics, shifting the paradigm from static connectivity estimation to the analysis of continuous temporal topology in epileptic networks.