Abstract
Modeling the dynamics of interacting entities using an evolving graph is an essential problem in fields such as financial networks and e-commerce. Traditional approaches focus primarily on pairwise interactions, limiting their ability to capture the complexity of real-world interactions involving multiple entities and their intricate relationship structures. This work addresses the problem of forecasting higher-order interaction events in multi-relational recursive hypergraphs. This is done using a dynamic graph representation learning framework that can capture complex relationships involving multiple entities. The proposed model, \textit{Relational Recursive Hyperedge Temporal Point Process} (RRHyperTPP) uses an encoder that learns a dynamic node representation based on the historical interaction patterns and then a hyperedge link prediction based decoder to model the event's occurrence. These learned representations are then used for downstream tasks involving forecasting the type and time of interactions. The main challenge in learning from hyperedge events is that the number of possible hyperedges grows exponentially with the number of nodes in the network. This will make the computation of negative log-likelihood of the temporal point process expensive, as the calculation of survival function requires a summation over all possible hyperedges. In our work, we use noise contrastive estimation to learn the parameters of our model, and we have experimentally shown that our models perform better than previous state-of-the-art methods for interaction forecasting.
Abstract (translated)
使用一个随机的图来建模交互实体之间的动态关系是金融网络和电子商务等领域中一个至关重要的任务。传统的解决方案主要关注一对一交互，限制了它们对涉及多个实体及其复杂关系结构的现实交互的捕捉能力。本文解决了在多关系递归超图中预测更高阶交互事件的问题。这是通过使用动态图表示学习框架来实现的，该框架可以捕捉涉及多个实体的复杂关系。所提出的模型《关系递归超网时空点过程》（RRHyperTPP）使用一个编码器，根据历史交互模式学习动态节点表示，然后使用解码器基于预测事件的发生来建模其发生。这些学习到的表示随后用于下游任务，包括预测交互的类型和时间。学习从超边缘事件的主要挑战是，随着网络中节点数的增加，可能的超边数呈指数增长。这将使得计算时间点过程的负对数似然函数变得昂贵，因为计算生存函数需要对所有可能的超边进行求和。在我们的工作中，我们使用噪声对比估计来学习我们的模型的参数，并通过实验已经证明了我们的模型在交互预测方面优于以前的先进方法。
URL
https://arxiv.org/abs/2404.17943
