  Dynamic graph learning equips the edges with time attributes and allows
multiple links between two nodes, which is a crucial technology for
understanding evolving data scenarios like traffic prediction and
recommendation systems. Existing works obtain the evolving patterns mainly
depending on the most recent neighbor sequences. However, we argue that whether
two nodes will have interaction with each other in the future is highly
correlated with the same interaction that happened in the past. Only
considering the recent neighbors overlooks the phenomenon of repeat behavior
and fails to accurately capture the temporal evolution of interactions. To fill
this gap, this paper presents RepeatMixer, which considers evolving patterns of
first and high-order repeat behavior in the neighbor sampling strategy and
temporal information learning. Firstly, we define the first-order repeat-aware
nodes of the source node as the destination nodes that have interacted
historically and extend this concept to high orders as nodes in the destination
node's high-order neighbors. Then, we extract neighbors of the source node that
interacted before the appearance of repeat-aware nodes with a slide window
strategy as its neighbor sequence. Next, we leverage both the first and
high-order neighbor sequences of source and destination nodes to learn temporal
patterns of interactions via an MLP-based encoder. Furthermore, considering the
varying temporal patterns on different orders, we introduce a time-aware
aggregation mechanism that adaptively aggregates the temporal representations
from different orders based on the significance of their interaction time
sequences. Experimental results demonstrate the superiority of RepeatMixer over
state-of-the-art models in link prediction tasks, underscoring the
effectiveness of the proposed repeat-aware neighbor sampling strategy.
