Large-scale embedding learning in heterogeneous event data

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Heterogeneous events, which are defined as events connecting strongly-Typed objects, are ubiquitous in the real world.We propose a HyperEdge-Based Embedding (HEBE) framework for heterogeneous event data, where a hyperedge represents the interaction among a set of involving objects in an event. The HEBE framework models the proximity among objects in an event by predicting a target object given the other participating objects in the event (hyperedge). Since each hyperedge encapsulates more information on a given event, HEBE is robust to data sparseness. In addition, HEBE is scalable when the data size spirals. Extensive experiments on large-scale real-world datasets demonstrate the efficacy and robustness of HEBE.

Original languageEnglish (US)
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages907-912
Number of pages6
ISBN (Electronic)9781509054725
DOIs
StatePublished - Jan 31 2017
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: Dec 12 2016Dec 15 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other16th IEEE International Conference on Data Mining, ICDM 2016
CountrySpain
CityBarcelona, Catalonia
Period12/12/1612/15/16

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Gui, H., Liu, J., Tao, F., Jiang, M., Norick, B., & Han, J. (2017). Large-scale embedding learning in heterogeneous event data. In F. Bonchi, X. Wu, R. Baeza-Yates, J. Domingo-Ferrer, & Z-H. Zhou (Eds.), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016 (pp. 907-912). [7837924] (Proceedings - IEEE International Conference on Data Mining, ICDM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2016.42