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Related Experiment Video

Updated: Jul 12, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Published on: November 1, 2019

Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark.

Carl Yang¹, Yuxin Xiao¹, Yu Zhang¹

¹Carl Yang is with Emory University; Yuxin Xiao is with Carnegie Mellon University; Yu Zhang and Jiawei Han are with University of Illinois, Urbana Champaign; Yizhou Sun is with University of California, Los Angeles.

IEEE Transactions on Knowledge and Data Engineering

|November 2, 2023

Summary

This study unifies and evaluates heterogeneous network embedding (HNE) algorithms. It introduces a framework, benchmark datasets, and refactored code to standardize comparisons and advance HNE research.

Keywords:

benchmark heterogeneous network representation learning survey

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Area of Science:

Data Science
Network Science
Machine Learning

Background:

Heterogeneous networks model complex real-world interactions.
Representation learning (embedding) is crucial for network analysis.
Existing heterogeneous network embedding (HNE) algorithms lack standardized evaluation.

Purpose of the Study:

To provide a unified framework for summarizing and evaluating HNE research.
To establish a systematic categorization and analysis of existing HNE algorithms.
To create a benchmark platform for fair and comprehensive HNE algorithm comparison.

Main Methods:

Developed a generic paradigm for categorizing HNE algorithms.
Created four diverse benchmark datasets for evaluation.
Refactored and standardized implementations of 13 popular HNE algorithms.

Main Results:

Established a unified framework for HNE algorithm analysis.
Provided benchmark datasets facilitating fair comparisons.
Released open-source code and data for community use.

Conclusions:

The unified framework and benchmark platform offer a universal reference for HNE research.
Standardized evaluation is essential for attributing performance gains to algorithm design.
Open-sourcing data and code promotes reproducible research and community development.