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Efficient link prediction in continuous-time dynamic networks is achallenging problem that has attracted much research attention in recent years.A widely used approach to dynamic network link prediction is to extract thelocal structure of the target link through temporal random walk on the networkand learn node features using a coding model. However, this approach oftenassumes that candidate temporal neighbors follow some certain types ofdistributions, which may be inappropriate for real-world networks, therebyincurring information loss. To address this limitation, we propose a frameworkin continuous-time dynamic networks based on Optimal Transmission (OT) andMetropolis Hastings (MH) sampling (COM). Specifically, we use optimaltransmission theory to calculate the Wasserstein distance between the currentnode and the time-valid candidate neighbors to minimize information loss innode information propagation. Additionally, we employ the MH algorithm toobtain higher-order structural relationships in the vicinity of the targetlink, as it is a Markov Chain Monte Carlo method and can flexibly simulatetarget distributions with complex patterns. We demonstrate the effectiveness ofour proposed method through experiments on eight datasets from differentfields.

Efficient Link Prediction in Continuous-Time Dynamic Networks using Optimal Transmission and Metropolis Hastings Sampling