Embedding Learning with Events in Heterogeneous Information Networks.
Embedding Learning with Events in Heterogeneous Information Networks.
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DOI：
10.1109/tkde.2017.2733530
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发表时间：
2017-11
影响因子：
8.9
通讯作者：
Han J
中科院分区：
文献类型：
--
作者：
Gui H;Liu J;Tao F;Jiang M;Norick B;Kaplan L;Han J
在实际应用中，多种类型的对象相互连接，形成异构信息网络。在这样的异构信息网络中，我们的关键观察，许多互动发生由于一些事件和每个事件中的对象形成一个完整的语义单位。通过利用这样的属性，我们提出了一个通用的框架，称为HyperEdge-Based Embedding（Hebe）学习对象嵌入与异构信息网络中的事件，其中一个超边缘包括参与一个事件的对象。Hebe框架使用两种方法对每个事件中对象之间的接近度进行建模：（1）在给定事件中的其他参与对象的情况下预测目标对象，以及（2）在给定所有参与对象的情况下预测事件是否可以被观察到。由于每个超边缘封装了给定事件的更多信息，因此Hebe对数据稀疏和噪声具有鲁棒性。此外，当数据大小螺旋上升时，Hebe是可伸缩的。在大规模真实数据集上的大量实验表明了该框架的有效性和鲁棒性。
In real-world applications, objects of multiple types are interconnected, forming Heterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to some event and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called HyperEdge-Based Embedding (Hebe) to learn object embeddings with events in heterogeneous information networks, where a hyperedge encompasses the objects participating in one event. The Hebe framework models the proximity among objects in each event with two methods: (1) predicting a target object given other participating objects in the event, and (2) predicting if the event can be observed given all the participating objects. Since each hyperedge encapsulates more information of a given event, Hebe is robust to data sparseness and noise. In addition, Hebe is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.