Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: Visual Analytics of Heterogeneous Data Using Hypergraph Learning

Abstract

For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.

Authors:
 [1];  [1];  [2];  [1]
+ Show Author Affiliations
  1. Stony Brook Univ., Stony Brook, NY (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
OSTI Identifier:
1496572
Report Number(s):
BNL-211294-2019-JAAM
Journal ID: ISSN 2157-6904
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Intelligent Systems and Technology
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2157-6904
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; hypergraph learning; data fusion; high-dimensional data

Citation Formats

Xie, Cong, Zhong, Wen, Xu, Wei, and Mueller, Klaus. Visual Analytics of Heterogeneous Data Using Hypergraph Learning. United States: N. p., 2018. Web. doi:10.1145/3200765.
Copy to clipboard
Xie, Cong, Zhong, Wen, Xu, Wei, & Mueller, Klaus. Visual Analytics of Heterogeneous Data Using Hypergraph Learning. United States. https://doi.org/10.1145/3200765
Copy to clipboard
Xie, Cong, Zhong, Wen, Xu, Wei, and Mueller, Klaus. Sat . "Visual Analytics of Heterogeneous Data Using Hypergraph Learning". United States. https://doi.org/10.1145/3200765. https://www.osti.gov/servlets/purl/1496572.
Copy to clipboard
@article{osti_1496572,
title = {Visual Analytics of Heterogeneous Data Using Hypergraph Learning},
author = {Xie, Cong and Zhong, Wen and Xu, Wei and Mueller, Klaus},
abstractNote = {For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. Thesemodels represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach forthe classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets.To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. Here, we demonstrate the capability of our approach via two real-world cases.},
doi = {10.1145/3200765},
journal = {ACM Transactions on Intelligent Systems and Technology},
number = 1,
volume = 10,
place = {United States},
year = {Sat Dec 01 00:00:00 EST 2018},
month = {Sat Dec 01 00:00:00 EST 2018}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1145/3200765
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referenced in this record:

Bubble Sets: Revealing Set Relations with Isocontours over Existing Visualizations
journal, November 2009

  • Collins, C.; Penn, G.; Carpendale, S.
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 15, Issue 6
  • DOI: 10.1109/TVCG.2009.122

Convex Optimization
book, January 2004

The Data Context Map: Fusing Data and Attributes into a Unified Display
journal, January 2016

  • Cheng, Shenghui; Mueller, Klaus
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 22, Issue 1
  • DOI: 10.1109/TVCG.2015.2467552

Tag-based social image search with visual-text joint hypergraph learning
conference, January 2011

  • Gao, Yue; Wang, Meng; Luan, Huanbo
  • Proceedings of the 19th ACM international conference on Multimedia - MM '11
  • DOI: 10.1145/2072298.2072054

OnSet: A Visualization Technique for Large-scale Binary Set Data
journal, December 2014

  • Sadana, Ramik; Major, Timothy; Dove, Alistair
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12
  • DOI: 10.1109/TVCG.2014.2346249

A Utility-Aware Visual Approach for Anonymizing Multi-Attribute Tabular Data
journal, January 2018

  • Wang, Xumeng; Chou, Jia-Kai; Chen, Wei
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 24, Issue 1
  • DOI: 10.1109/TVCG.2017.2745139

Resource description framework: metadata and its applications
journal, July 2001

  • Candan, K. Selçuk; Liu, Huan; Suvarna, Reshma
  • ACM SIGKDD Explorations Newsletter, Vol. 3, Issue 1
  • DOI: 10.1145/507533.507536

UpSet: Visualization of Intersecting Sets
journal, December 2014

  • Lex, Alexander; Gehlenborg, Nils; Strobelt, Hendrik
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 20, Issue 12
  • DOI: 10.1109/TVCG.2014.2346248

Visualization of Heterogeneous Data
journal, November 2007

  • Cammarano, Mike; Dong, Xin; Chan, Bryan
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 13, Issue 6
  • DOI: 10.1109/TVCG.2007.70617

Hypergraph spectral learning for multi-label classification
conference, January 2008

  • Sun, Liang; Ji, Shuiwang; Ye, Jieping
  • Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 08
  • DOI: 10.1145/1401890.1401971

Link mining: a survey
journal, December 2005

  • Getoor, Lise; Diehl, Christopher P.
  • ACM SIGKDD Explorations Newsletter, Vol. 7, Issue 2
  • DOI: 10.1145/1117454.1117456

News recommendation via hypergraph learning: encapsulation of user behavior and news content
conference, January 2013

  • Li, Lei; Li, Tao
  • Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13
  • DOI: 10.1145/2433396.2433436

Exploring the Possibilities of Embedding Heterogeneous Data Attributes in Familiar Visualizations
journal, January 2017

  • Loorak, Mona Hosseinkhani; Perin, Charles; Collins, Christopher
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 23, Issue 1
  • DOI: 10.1109/TVCG.2016.2598586

Inductively Generating Euler Diagrams
journal, January 2011

  • Stapleton, G.; Rodgers, P.; Howse, J.
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 17, Issue 1
  • DOI: 10.1109/TVCG.2010.28

Visualizing set concordance with permutation matrices and fan diagrams
journal, December 2007

Hypergraph with sampling for image retrieval
journal, October 2011

Uncertainty-Aware Multidimensional Ensemble Data Visualization and Exploration
journal, September 2015

  • Chen, Haidong; Zhang, Song; Chen, Wei
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 21, Issue 9
  • DOI: 10.1109/TVCG.2015.2410278

Graph drawing by force-directed placement
journal, November 1991

  • Fruchterman, Thomas M. J.; Reingold, Edward M.
  • Software: Practice and Experience, Vol. 21, Issue 11
  • DOI: 10.1002/spe.4380211102

Music recommendation by unified hypergraph: combining social media information and music content
conference, January 2010

  • Bu, Jiajun; Tan, Shulong; Chen, Chun
  • Proceedings of the international conference on Multimedia - MM '10
  • DOI: 10.1145/1873951.1874005

KelpFusion: A Hybrid Set Visualization Technique
journal, November 2013

  • Meulemans, Wouter; Riche, Nathalie Henry; Speckmann, Bettina
  • IEEE Transactions on Visualization and Computer Graphics, Vol. 19, Issue 11
  • DOI: 10.1109/TVCG.2013.76

Refinery: Visual Exploration of Large, Heterogeneous Networks through Associative Browsing
journal, June 2015

  • Kairam, S.; Riche, N. H.; Drucker, S.
  • Computer Graphics Forum, Vol. 34, Issue 3
  • DOI: 10.1111/cgf.12642

On the effect of hyperedge weights on hypergraph learning
journal, January 2017

A visual analytical approach for transfer learning in classification
journal, June 2017

    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: