Large-scale embedding learning in heterogeneous event data

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

doi:10.1109/ICDM.2016.42

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 proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
Editors	Francesco Bonchi, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	907-912
Number of pages	6
ISBN (Electronic)	9781509054725
DOIs	https://doi.org/10.1109/ICDM.2016.42
State	Published - Jul 2 2016
Event	16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain Duration: Dec 12 2016 → Dec 15 2016

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Volume	0
ISSN (Print)	1550-4786

Other

Other	16th IEEE International Conference on Data Mining, ICDM 2016
Country/Territory	Spain
City	Barcelona, Catalonia
Period	12/12/16 → 12/15/16

ASJC Scopus subject areas

General Engineering

Online availability

10.1109/ICDM.2016.42

Library availability

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Gui, H., Liu, J., Tao, F., Jiang, M., Norick, B., & Han, J. (2016). Large-scale embedding learning in heterogeneous event data. In F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, Z.-H. Zhou, & X. Wu (Eds.), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016 (pp. 907-912). Article 7837924 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2016.42

Large-scale embedding learning in heterogeneous event data. / Gui, Huan; Liu, Jialu; Tao, Fangbo et al.
Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. ed. / Francesco Bonchi; Josep Domingo-Ferrer; Ricardo Baeza-Yates; Zhi-Hua Zhou; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 907-912 7837924 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gui, H, Liu, J, Tao, F, Jiang, M, Norick, B & Han, J 2016, Large-scale embedding learning in heterogeneous event data. in F Bonchi, J Domingo-Ferrer, R Baeza-Yates, Z-H Zhou & X Wu (eds), Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016., 7837924, Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 907-912, 16th IEEE International Conference on Data Mining, ICDM 2016, Barcelona, Catalonia, Spain, 12/12/16. https://doi.org/10.1109/ICDM.2016.42

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

Gui, Huan ; Liu, Jialu ; Tao, Fangbo et al. / Large-scale embedding learning in heterogeneous event data. Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016. editor / Francesco Bonchi ; Josep Domingo-Ferrer ; Ricardo Baeza-Yates ; Zhi-Hua Zhou ; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 907-912 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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AB - 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.

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Large-scale embedding learning in heterogeneous event data

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