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of aggregation routes and decaying the outdated information, allowing our model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.

在这篇论文中，我们通过引入自适应邻域编码机制来增强现有的时态图神经网络 (TGNs)，提出了 SEAN 模型，该模型可以无缝集成到现有的 TGNs 中，有效提升其性能。具体而言，该模型通过代表性邻居选择和时态感知邻域信息聚合两个阶段，实现自适应邻域编码的过程，从而在聚合过程中灵活利用重要的邻居信息，并适应性地选择和衰减过时信息，取得了出色的性能提升。我们在四个公共数据集和一个金融基准数据集上将 SEAN 与三种代表性 TGNs 进行了集成，并进行了大量的实验证明，SEAN 可以在所有模型上持续提高性能，达到顶级水平并具有出色的稳健性。