Neural embedding propagation on heterogeneous networks

Carl Yang, Jieyu Zhang, Jiawei Han

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


Classification is one of the most important problems in machine learning. To address label scarcity, semi-supervised learning (SSL) has been intensively studied over the past two decades, which mainly leverages data affinity modeled by networks. Label propagation (LP), however, as the most popular SSL technique, mostly only works on homogeneous networks with single-typed simple interactions. In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multi-typed objects and links, and thus endure multi-typed complex interactions. Specifically, we propose neural embedding propagation (NEP), which leverages distributed embeddings to represent objects and dynamically composed modular networks to model their complex interactions. While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns. Further, we develop a series of efficient training strategies for NEP, leading to its easy deployment on real-world heterogeneous networks with millions of objects. With extensive experiments on three datasets, we comprehensively demonstrate the effectiveness, efficiency, and robustness of NEP compared with state-of-the-art network embedding and SSL algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728146034
StatePublished - Nov 2019
Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
Duration: Nov 8 2019Nov 11 2019

Publication series

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


Conference19th IEEE International Conference on Data Mining, ICDM 2019


  • Embedding propagation
  • Heterogeneous networks
  • Label propagation
  • Modular networks
  • Network embedding
  • Semi supervised learning

ASJC Scopus subject areas

  • General Engineering


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