TY - GEN
T1 - Large-scale embedding learning in heterogeneous event data
AU - Gui, Huan
AU - Liu, Jialu
AU - Tao, Fangbo
AU - Jiang, Meng
AU - Norick, Brandon
AU - Han, Jiawei
N1 - Funding Information:
We wish to thank the anonymous reviewers for their helpful feedback of the manuscript. In addition, we wish to thank Jim Cai and Jingjing Wang for their patient proofreading. Research was sponsored in part by the U.S. Army Research Lab. under Cooperative Agreement No. W911NF- 09-2-0053 (NSCTA), National Science Foundation IIS-1320617, IIS-1354329 and IIS 16-18481, HDTRA1-10-1-0120, and grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov). The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies of the U.S. Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agencies.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
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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U2 - 10.1109/ICDM.2016.42
DO - 10.1109/ICDM.2016.42
M3 - Conference contribution
AN - SCOPUS:85014517501
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 907
EP - 912
BT - Proceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
A2 - Bonchi, Francesco
A2 - Domingo-Ferrer, Josep
A2 - Baeza-Yates, Ricardo
A2 - Zhou, Zhi-Hua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Data Mining, ICDM 2016
Y2 - 12 December 2016 through 15 December 2016
ER -