融合时间约束游走的记忆增强时序图神经网络

Time-constrained walk fused memory enhanced temporal graph neural network

关键词:  时序网络  游走  记忆  特征衰减  因果卷积

Key words:  temporal network  walk  memory  feature attenuation  causal convolution

摘要:

为解决现有时序图表示学习方法难以充分挖掘网络结构特征和依赖关系的问题,提出了一种融合时间约束游走的记忆增强时序图神经网络(time-constrained walk fused memory enhanced temporal graph neural network,TWMTGN)。首先,从交互节点出发构造特定类型的时间约束游走序列,采用记忆模块捕获网络的长期依赖关系,并将游走序列特征融合至节点记忆状态,在事件发生时进行动态更新；其次,根据节点类型和时间间隔设计特征衰减层,建模短期依赖关系,提高模型对关键历史交互节点的识别能力；最后,将目标节点聚合后的历史交互特征输入因果卷积网络,进一步挖掘特征之间的潜在关联。在真实数据集上的实验结果表明:所提网络能够提升时间链路预测任务的效果,且复杂度较小；时间约束游走长度和次数等参数会影响模型的性能。研究提出的时间约束游走序列能够捕捉网络结构特征,节点记忆和特征衰减层有助于捕获网络依赖关系。

Abstract:

To address the challenges faced by existing temporal graph representation learning methods in fully exploiting structural features and dependencies of the network, a time-constrained walk fused memory enhanced temporal graph neural network (TWMTGN) is proposed. Firstly, a specific type of temporal constraint walk sequence is constructed for the interaction node, and the memory module is used to capture the long-term dependence of the network. The walk sequence features are fused into the memory of interaction node, which is dynamically updated upon the occurance of events. Secondly, a feature attenuation layer is designed according to the node type and time interval to model short-term dependencies, which improves the model′s ability to identify key historical interaction nodes. Finally, the aggregated historical interaction features of the target node are fed into a causal convolutional network to further uncover the potential association between them. Experimental results on real datasets show that the proposed network can improve the performance of temporal link prediction tasks while maintaining relatively low complexity. Parameters such as the length and frequency of time-constrained walks affect the mode′s performance. The proposed time-constrained walk sequences can effectively capture the structural features of the network, while the node memory and feature attenuation layers help to capture the network dependencies.