Recently, representation learning over graph networks has gained popularity, with various models showing promising results. Despite this, several challenges persist: 1) most methods are designed for static or discrete-time dynamic graphs; 2) existing continuous-time dynamic graph algorithms focus on a single evolving perspective; and 3) many continuous-time dynamic graph approaches necessitate numerous temporal neighbors to capture long-term dependencies. In response, this paper introduces the Multi-Perspective Feedback-Attention Coupling (MPFA) model. MPFA incorporates information from both evolving and raw perspectives, efficiently learning the interleaved dynamics of observed processes. The evolving perspective employs temporal self-attention to distinguish continuously evolving temporal neighbors for information aggregation. Through dynamic updates, this perspective can capture long-term dependencies using a small number of temporal neighbors. Meanwhile, the raw perspective utilizes a feedback attention module with growth characteristic coefficients to aggregate raw neighborhood information. Experimental results on a self-organizing dataset and seven public datasets validate the efficacy and competitiveness of our proposed model.

翻译：近年来，图网络上的表示学习日益流行，各类模型展现出优异的性能。尽管如此，仍面临若干挑战：1) 多数方法针对静态或离散时间动态图设计；2) 现有连续时间动态图算法仅关注单一演化视角；3) 许多连续时间动态图方法需要大量时间邻居来捕获长期依赖。为此，本文提出多视角反馈-注意力耦合（MPFA）模型。MPFA融合了演化视角与原始视角的信息，高效学习观测过程中交织的动态特征。演化视角采用时间自注意力机制，区分持续演化的时间邻居以实现信息聚合。通过动态更新，该视角可利用少量时间邻居捕获长期依赖。同时，原始视角借助具有增长特性系数的反馈注意力模块，聚合原始邻居信息。在自组织数据集及七个公开数据集上的实验结果验证了所提模型的有效性与竞争力。