A Visual Analytics Framework for Contrastive Network Analysis

Takanori Fujiwara, Jian Zhao, Francine Chen, Kwan Liu Ma

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

Abstract

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate ContraNA through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages48-59
Number of pages12
ISBN (Electronic)9781728180090
DOIs
StatePublished - Oct 2020
Event15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020 - Virtual, Salt Lake City, United States
Duration: Oct 25 2020Oct 30 2020

Publication series

NameProceedings - 2020 IEEE Conference on Visual Analytics Science and Technology, VAST 2020

Conference

Conference15th IEEE Conference on Visual Analytics Science and Technology, VAST 2020
CountryUnited States
CityVirtual, Salt Lake City
Period10/25/2010/30/20

Keywords

  • Contrastive learning
  • interpretability
  • network comparison
  • network representation learning
  • visual analytics

ASJC Scopus subject areas

  • Media Technology
  • Modeling and Simulation

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