  The dynamics of temporal networks lie in the continuous interactions between
nodes, which exhibit the dynamic node preferences with time elapsing. The
challenges of mining temporal networks are thus two-fold: the dynamic structure
of networks and the dynamic node preferences. In this paper, we investigate the
dynamic graph sampling problem, aiming to capture the preference structure of
nodes dynamically in cooperation with GNNs. Our proposed Dynamic Preference
Structure (DPS) framework consists of two stages: structure sampling and graph
fusion. In the first stage, two parameterized samplers are designed to learn
the preference structure adaptively with network reconstruction tasks. In the
second stage, an additional attention layer is designed to fuse two sampled
temporal subgraphs of a node, generating temporal node embeddings for
downstream tasks. Experimental results on many real-life temporal networks show
that our DPS outperforms several state-of-the-art methods substantially owing
to learning an adaptive preference structure. The code will be released soon at
https://github.com/doujiang-zheng/Dynamic-Preference-Structure.
