  Evolving temporal networks serve as the abstractions of many real-life
dynamic systems, e.g., social network and e-commerce. The purpose of temporal
network embedding is to map each node to a time-evolving low-dimension vector
for downstream tasks, e.g., link prediction and node classification. The
difficulty of temporal network embedding lies in how to utilize the topology
and time information jointly to capture the evolution of a temporal network. In
response to this challenge, we propose a temporal motif-preserving network
embedding method with bicomponent neighbor aggregation, named TME-BNA.
Considering that temporal motifs are essential to the understanding of topology
laws and functional properties of a temporal network, TME-BNA constructs
additional edge features based on temporal motifs to explicitly utilize complex
topology with time information. In order to capture the topology dynamics of
nodes, TME-BNA utilizes Graph Neural Networks (GNNs) to aggregate the
historical and current neighbors respectively according to the timestamps of
connected edges. Experiments are conducted on three public temporal network
datasets, and the results show the effectiveness of TME-BNA.
