This paper proposes a model of information cascades as directed spanning trees (DSTs) over observed documents. In addition, we propose a contrastive training procedure that exploits partial temporal ordering of node infections in lieu of labeled training links. This combination of model and unsupervised training makes it possible to improve on models that use infection times alone and to exploit arbitrary features of the nodes and of the text content of messages in information cascades. With only basic node and time lag features similar to previous models, the DST model achieves performance with unsupervised training comparable to strong baselines on a blog network inference task. Unsupervised training with additional content features achieves significantly better results, reaching half the accuracy of a fully supervised model.

本文提出了一个信息级联的模型，它将观察到的文档作为有向跨度树。同时，我们还提出了一个对比训练程序，利用节点感染的部分时间顺序而不是标记的训练链接。这种模型和无监督训练的组合使得可以改进仅使用感染时间模型的模型，并利用信息级联消息的节点和文本内容的任意特征。基于先前模型类似的基本节点和时间延迟特征，DST模型在博客网络推断任务上表现与强基线相当。使用其他内容特征的无监督训练可以实现更好的结果，达到完全监督模型精度的一半。

信息传播模型的对比训练