论文标题
连续时间网络的相互激动人心的潜在太空鹰队过程模型
A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks
论文作者
论文摘要
网络和时间点过程是在各个域中建模复杂动态关系数据的基本构件。我们建议使用节点的潜在空间表示形式，这是一种潜在空间鹰队（LSH）模型，这是一种连续时间的关系网络的新型生成模型。我们使用共同令人兴奋的霍克斯工艺在节点之间建模关系事件，其基线强度取决于潜在空间中的节点与发件人和接收器特定效果之间的距离。我们证明，我们提出的LSH模型可以复制在包括互惠和传递性在内的真实时间网络中观察到的许多功能，同时还可以实现卓越的预测准确性并提供比现有模型更明显的拟合。
Networks and temporal point processes serve as fundamental building blocks for modeling complex dynamic relational data in various domains. We propose the latent space Hawkes (LSH) model, a novel generative model for continuous-time networks of relational events, using a latent space representation for nodes. We model relational events between nodes using mutually exciting Hawkes processes with baseline intensities dependent upon the distances between the nodes in the latent space and sender and receiver specific effects. We demonstrate that our proposed LSH model can replicate many features observed in real temporal networks including reciprocity and transitivity, while also achieving superior prediction accuracy and providing more interpretable fits than existing models.
