BayesMem-CD: Learning Stable Dynamic Graph Representations with Bayesian Temporal Memory

Dynamic community detection is confronted with a fundamental ambiguity: an observed structural change may be indicative of genuine community evolution or merely transient graph noise. This issue is typically resolved by existing methods through deterministic temporal smoothing or snapshot-wise inference, which can either suppress meaningful structural transitions or generate unstable assignments. Additionally, deterministic temporal representations offer restricted insight into the periods during which historical evidence is uncertain. We introduce BayesMem-CD, an uncertainty-aware framework that is based on Bayesian temporal memory. This framework conceptualizes community evolution as a probabilistic temporal inference, rather than a series of deterministic clustering decisions. BayesMem-CD quantifies epistemic uncertainty and regulates temporal adaptation based on the reliability of observed structure by maintaining long-range structural evidence through graph memory and representing community assignments probabilistically. Spurious assignment changes are further discouraged by short-term temporal regularization, which does not preclude persistent evidence from driving genuine community transitions. Experiments conducted on synthetic and real-world dynamic networks have shown that BayesMem-CD is capable of achieving robust community recovery and temporal stability, as well as better-calibrated uncertainty under structural perturbations. The complementary benefits of probabilistic inference, temporal memory, and short-term consistency are further demonstrated by ablation and sensitivity analyzes. These findings indicate that a principled approach to distinguishing structural evolution from transitory variation in dynamic graphs is provided by explicitly modeling uncertainty in temporal evidence.