  Temporal networks are an important type of network whose topological
structure changes over time. Compared with methods on static networks, temporal
network embedding (TNE) methods are facing three challenges: 1) it cannot
describe the temporal dependence across network snapshots; 2) the node
embedding in the latent space fails to indicate changes in the network
topology; and 3) it cannot avoid a lot of redundant computation via parameter
inheritance on a series of snapshots. To this end, we propose a novel temporal
network embedding method named Dynamic Cluster Structure Constraint model
(DyCSC), whose core idea is to capture the evolution of temporal networks by
imposing a temporal constraint on the tendency of the nodes in the network to a
given number of clusters. It not only generates low-dimensional embedding
vectors for nodes but also preserves the dynamic nonlinear features of temporal
networks. Experimental results on multiple realworld datasets have demonstrated
the superiority of DyCSC for temporal graph embedding, as it consistently
outperforms competing methods by significant margins in multiple temporal link
prediction tasks. Moreover, the ablation study further validates the
effectiveness of the proposed temporal constraint.
