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Related Experiment Video

Updated: Feb 16, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Embedding Learning with Events in Heterogeneous Information Networks.

Huan Gui¹, Jialu Liu², Fangbo Tao¹

¹University of Illinois at Urbana-Champaign, Urbana, IL, USA.

IEEE Transactions on Knowledge and Data Engineering

|December 16, 2017

Summary

This study introduces Hyperedge-Based Embedding (Hebe), a novel framework for learning object representations in heterogeneous information networks by modeling events as hyperedges. Hebe effectively captures object relationships within events, proving robust and scalable for real-world data.

Keywords:

Event Heterogeneous Information Networks Large Scale Noise Pairwise Ranking Object Embedding

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Published on: July 1, 2014

Area of Science:

Data Science
Network Science
Machine Learning

Background:

Real-world applications involve complex objects interconnected in Heterogeneous Information Networks (HINs).
Object interactions in HINs often occur within specific events, forming cohesive semantic units.

Purpose of the Study:

To propose a generic framework, Hyperedge-Based Embedding (Hebe), for learning object embeddings in HINs.
To leverage the event-centric property of HINs for improved representation learning.

Main Methods:

Hebe utilizes hyperedges to represent events, encompassing all participating objects.
Object proximity is modeled through two prediction tasks: predicting a target object from others in an event, and predicting event observability from all participating objects.

Main Results:

The Hebe framework demonstrates robustness against data sparseness and noise due to hyperedge encapsulation.
Experiments on large-scale real-world datasets confirm the efficacy and robustness of Hebe.

Conclusions:

Hebe offers an effective and scalable approach for learning object embeddings in heterogeneous information networks.
The event-based hyperedge modeling captures rich semantic information, enhancing representation learning.