用于预测时序网络中递归与多关系事件的深度表示学习
Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks
AI总结：
本文提出RRHyperTPP模型，通过时序超图表示学习框架解决多关系和递归高阶交互事件的预测问题，并采用噪声对比估计方法优化模型参数，在交互预测任务上优于现有最先进方法。
摘要：
理解实体间交互产生的关系可能非常困难，而预测它们则更具挑战性。这个问题在金融网络和电子商务等各个领域都有许多应用。这些关系可能涉及比仅仅包含两个以上实体更多的复杂性。其中一种场景是多个实体之间不断演变的递归关系，到目前为止，这仍然是一个开放问题。这项工作解决了预测可以是多关系和递归的高阶交互事件的问题。我们将问题置于能够捕捉涉及多个实体的复杂关系的时序超图表示学习框架中。提出的模型 Relational Recursive Hyperedge Temporal Point Process (RRHyperTPP) 使用一个基于历史交互模式学习动态节点表示的编码器，然后使用一个基于超边链接预测的解码器来建模交互事件的发生。这些学习到的表示随后用于涉及预测交互类型和时间的下游任务。从超边事件中学习的主要挑战在于，可能的超边数量随网络中节点数量呈指数增长。这将使得计算时间点过程的负对数似然变得昂贵，因为生存函数的计算需要对所有可能的超边进行求和。在我们的工作中，我们开发了一种噪声对比估计方法来学习模型的参数，并且我们在实验中表明，我们的模型在交互预测方面优于之前的最先进方法。
英文摘要：
Understanding relations arising out of interactions among entities can be very difficult, and predicting them is even more challenging. This problem has many applications in various fields, such as financial networks and e-commerce. These relations can involve much more complexities than just involving more than two entities. One such scenario is evolving recursive relations between multiple entities, and so far, this is still an open problem. This work addresses the problem of forecasting higher-order interaction events that can be multi-relational and recursive. We pose the problem in the framework of representation learning of temporal hypergraphs 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 occurrence of interaction events. 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 develop a noise contrastive estimation method 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.
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