CO2Vec: Embeddings of co-ordered networks based on mutual reinforcement

Meng Fen Chiang, Ee Peng Lim, Wang Chien Lee, Philips Kokoh Prasetyo

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

Abstract

We study the problem of representation learning for multiple types of entities in a co-ordered network where order relations exist among entities of the same type, and association relations exist across entities of different types. The key challenge in learning co-ordered network embedding is to preserve order relations among entities of the same type while leveraging on the general consistency in order relations between different entity types. In this paper, we propose an embedding model, CO2Vec, that addresses this challenge using mutually reinforced order dependencies. Specifically, CO2Vec explores in-direct order dependencies as supplementary evidence to enhance order representation learning across different types of entities. We conduct extensive experiments on both synthetic and real world datasets to demonstrate the robustness and effectiveness of CO2Vec against several strong baselines in link prediction task. We also design a comprehensive evaluation framework to study the performance of CO2Vec under different settings. In particular, our results show the robustness of CO2Vec with the removal of order relations from the original networks.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020
EditorsGeoff Webb, Zhongfei Zhang, Vincent S. Tseng, Graham Williams, Michalis Vlachos, Longbing Cao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages148-157
Number of pages10
ISBN (Electronic)9781728182063
DOIs
StatePublished - Oct 2020
Event7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020 - Virtual, Sydney, Australia
Duration: Oct 6 2020Oct 9 2020

Publication series

NameProceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020

Conference

Conference7th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2020
CountryAustralia
CityVirtual, Sydney
Period10/6/2010/9/20

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Analysis
  • Discrete Mathematics and Combinatorics

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