Embedding Learning with Events in Heterogeneous Information Networks
Huan Gui, Jialu Liu, Fangbo Tao, Meng Jiang, Brandon Norick, Lance Kaplan, Jiawei Han · IEEE Transactions on Knowledge and Data Engineering · 2017
In real-world applications, objects of multiple types are interconnected, formingHeterogeneous Information Networks. In such heterogeneous information networks, we make the key observation that many interactions happen due to someeventand the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework calledHyperEdge-BasedEmbedding(Hebe) to learn object embeddings with events in heterogeneous information networks, where ahyperedgeencompasses the objects participating in one event. TheHebeframework 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,Hebeis robust to data sparseness and noise. In addition,Hebeis scalable when the data size spirals. Extensive experiments on large-scale real-world datasets show the efficacy and robustness of the proposed framework.