Modeling temporal event sequences on the vertices of a network is an important problem with widespread applications; examples include modeling influences in social networks, preventing crimes by modeling their space–time occurrences, and forecasting earthquakes. Existing solutions for this problem use a parametric approach, whose applicability is limited to event sequences following some well‐known distributions, which is not true for many real life event datasets. To overcome this limitation, in this work, we propose a composite recurrent neural network model for learning events occurring in the vertices of a network over time. Our proposed model combines two long short‐term memory units to capture base intensity and conditional intensity of an event sequence. We also introduce a second‐order statistic loss that penalizes higher divergence between the generated and the target sequence's distribution of hop count distance of consecutive events. Given a sequence of vertices of a network in which an event has occurred, the proposed model predicts the vertex where the next event would most likely occur. Experimental results on synthetic and real‐world datasets validate the superiority of our proposed model in comparison to various baseline methods.

中文翻译：

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使用长短期记忆和二阶统计损失学习网络事件序列

在网络的顶点上对时间事件序列进行建模是广泛应用中的一个重要问题。例如，对社交网络中的影响进行建模，通过对时空事件进行建模来预防犯罪以及预测地震。针对该问题的现有解决方案使用参数化方法，其适用性仅限于遵循某些众所周知的分布的事件序列，这对于许多现实生活中的事件数据集而言并非如此。为了克服这一局限性，在这项工作中，我们提出了一种复合递归神经网络模型，用于学习随着时间推移在网络顶点发生的事件。我们提出的模型结合了两个长短期记忆单元来捕获事件序列的基本强度和条件强度。我们还引入了二阶统计损失，该损失对连续事件的跳数计数距离的生成序列和目标序列的分布之间较高的偏差进行了惩罚。给定发生事件的网络的一系列顶点，建议的模型将预测下一个事件最有可能发生的顶点。综合和真实数据集上的实验结果证实了我们提出的模型与各种基准方法相比的优越性。