Embedding learning with events in heterogeneous information networks

Huan Gui, Jialu Liu, Fangbo Tao, Meng Jiang, Brandon Norick, Lance Kaplan, Jiawei Han

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number7997782
Pages (from-to)2428-2441
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
StatePublished - Nov 2017


  • Event
  • Heterogeneous Information Networks
  • Large Scale
  • Noise Pairwise Ranking
  • Object Embedding

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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