Ke Xu

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution drifts continually over time. Prevailing solutions rely on training auxiliary generative modules to synthesize memory graphs for cross-domain adaptation, which incurs su...