当前位置:
X-MOL 学术
›
IEEE Trans. Knowl. Data. Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Embedding Learning with Events in Heterogeneous Information Networks
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2017-11-01 , DOI: 10.1109/tkde.2017.2733530 Huan Gui 1 , Jialu Liu 2 , Fangbo Tao 1 , Meng Jiang 1 , Brandon Norick 1 , Lance Kaplan 3 , Jiawei Han 1
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2017-11-01 , DOI: 10.1109/tkde.2017.2733530 Huan Gui 1 , Jialu Liu 2 , Fangbo Tao 1 , Meng Jiang 1 , Brandon Norick 1 , Lance Kaplan 3 , Jiawei Han 1
Affiliation
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 H yperE dge- B ased E mbedding (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.
中文翻译:
在异构信息网络中嵌入事件学习
在实际应用中,多种类型的对象相互关联,形成异构信息网络 . 在这样的异构信息网络中,我们做出了关键的观察,即许多交互是由于某些原因而发生的。事件 每个事件中的对象构成一个完整的语义单元。通过利用这样的特性,我们提出了一个通用框架,称为H yper乙 边缘- 乙 阿塞德 乙 嵌入 (他是 ) 在异构信息网络中学习带有事件的对象嵌入,其中 超边 包括参与一个事件的对象。这他是 框架使用两种方法对每个事件中的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是否可以观察到该事件。由于每个超边封装了给定事件的更多信息,他是 对数据稀疏性和噪声具有鲁棒性。此外,他是 当数据大小螺旋上升时是可扩展的。对大规模真实世界数据集的大量实验表明了所提出框架的有效性和稳健性。
更新日期:2017-11-01
中文翻译:
在异构信息网络中嵌入事件学习
在实际应用中,多种类型的对象相互关联,形成