  The burgeoning field of dynamic graph representation learning, fuelled by the
increasing demand for graph data analysis in real-world applications, poses
both enticing opportunities and formidable challenges. Despite the promising
results achieved by recent research leveraging recurrent neural networks (RNNs)
and graph neural networks (GNNs), these approaches often fail to adequately
consider the impact of the edge temporal states on the strength of inter-node
relationships across different time slices, further overlooking the dynamic
changes in node features induced by fluctuations in relationship strength.
Furthermore, the extraction of global structural features is hindered by the
inherent over-smoothing drawback of GNNs, which in turn limits their overall
performance. In this paper, we introduce a novel dynamic graph representation
learning framework namely Recurrent Structure-reinforced Graph Transformer
(RSGT), which initially models the temporal status of edges explicitly by
utilizing different edge types and weights based on the differences between any
two consecutive snapshots. In this manner, the varying edge temporal states are
mapped as a part of the topological structure of the graph. Subsequently, a
structure-reinforced graph transformer is proposed to capture temporal node
representations that encoding both the graph topological structure and evolving
dynamics,through a recurrent learning paradigm. Our experimental evaluations,
conducted on four real-world datasets, underscore the superior performance of
the RSGT in the realm of discrete dynamic graph representation learning. The
results reveal that RSGT consistently surpasses competing methods in dynamic
link prediction tasks.
