Abstract: Dynamic network link prediction is one of the core issues in understanding and analyzing dynamic networks.In response to the challenges of capturing complex network structures and real evolution patterns faced by link prediction,this paper proposes a method integrating the graph neural network and neural ordinary differential equation to adaptive model various network dynamics:double-layer activity-constrained neural ordinary differential equation model(DANOM).DANOM integrates the importance and relative positional information of nodes to enhance the representation of network structures,strengthens the learning process of evolution patterns through neural ordinary differential equation units constrained by node activity,and mines effective information of the network under the reconstruction loss of node activity and node representation.DANOM achieves optimal results in various down-stream tasks on multiple real-world datasets.It achieves the highest improvements of 14% and 9.7% in terms of AUC and AP,respectively,in the single-step link prediction task.In cases of snapshot missingness,the average AUC and AP of link prediction are only reduced by 0.43% and 0.03%,respectively.In the user stitching experiments,DANOM achieves the highest improvements of 20.6% and 24.4% in terms of AUC and AP,respectively.

Key words: Graph representation learning, Dynamic network, Link prediction, Neural ordinary differential equation, Network dynamics