  Temporal Graph Networks (TGNs) have demonstrated their remarkable performance
in modeling temporal interaction graphs. These works can generate temporal node
representations by encoding the surrounding neighborhoods for the target node.
However, an inherent limitation of existing TGNs is their reliance on fixed,
hand-crafted rules for neighborhood encoding, overlooking the necessity for an
adaptive and learnable neighborhood that can accommodate both personalization
and temporal evolution across different timestamps. In this paper, we aim to
enhance existing TGNs by introducing an adaptive neighborhood encoding
mechanism. We present SEAN, a flexible plug-and-play model that can be
seamlessly integrated with existing TGNs, effectively boosting their
performance. To achieve this, we decompose the adaptive neighborhood encoding
process into two phases: (i) representative neighbor selection, and (ii)
temporal-aware neighborhood information aggregation. Specifically, we propose
the Representative Neighbor Selector component, which automatically pinpoints
the most important neighbors for the target node. It offers a tailored
understanding of each node's unique surrounding context, facilitating
personalization. Subsequently, we propose a Temporal-aware Aggregator, which
synthesizes neighborhood aggregation by selectively determining the utilization
of aggregation routes and decaying the outdated information, allowing our model
to adaptively leverage both the contextually significant and current
information during aggregation. We conduct extensive experiments by integrating
SEAN into three representative TGNs, evaluating their performance on four
public datasets and one financial benchmark dataset introduced in this paper.
The results demonstrate that SEAN consistently leads to performance
improvements across all models, achieving SOTA performance and exceptional
robustness.
