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g-Miner: Interactive Visual Group Mining on Multivariate Graphs

Authors:

Hanghang TongAuthors Info & Claims

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Pages 279 - 288

https://doi.org/10.1145/2702123.2702446

Published: 18 April 2015 Publication History

Abstract

With the rapid growth of rich network data available through various sources such as social media and digital archives,there is a growing interest in more powerful network visual analysis tools and methods. The rich information about the network nodes and links can be represented as multivariate graphs, in which the nodes are accompanied with attributes to represent the properties of individual nodes. An important task often encountered in multivariate network analysis is to uncover link structure with groups, e.g., to understand why a person fits a specific job or certain role in a social group well.The task usually involves complex considerations including specific requirement of node attributes and link structure, and hence a fully automatic solution is typically not satisfactory.In this work, we identify the design challenges for min-ing groups with complex criteria and present an interactive system, "g-Miner," that enables visual mining of groups on multivariate graph data. We demonstrate the effectiveness of our system through case study and in-depth expert inter-views. This work contributes to understanding the design of systems for leveraging users' knowledge progressively with algorithmic capacity for tackling massive heterogeneous information.

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Cited By

Han DZhu HChen WPan RLiu YZhou JFeng HZhang TWang XZhu MTao JFan CZhang X(2024)GraphFederator: Federated Visual Analysis for Multi-party Graphs2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00027(172-181)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00027
Wang SChen SCai TWu Y(2022)MULTI-NETVIS: Visual Analytics for Multivariate NetworkApplied Sciences10.3390/app1217840512:17(8405)Online publication date: 23-Aug-2022
https://doi.org/10.3390/app12178405
Ahn YYan MLin YChung WHwa R(2022)Tribe or Not? Critical Inspection of Group Differences Using TribalGramACM Transactions on Interactive Intelligent Systems10.1145/348450912:1(1-34)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3484509
Show More Cited By

Index Terms

g-Miner: Interactive Visual Group Mining on Multivariate Graphs
1. Human-centered computing

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Information & Contributors

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Published In

cover image ACM Conferences

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

April 2015

4290 pages

ISBN:9781450331456

DOI:10.1145/2702123

General Chairs:
Bo Begole
Huawei, USA
,
Jinwoo Kim
Yonsei University, Korea
,
Program Chairs:
Kori Inkpen
Microsoft Research, USA
,
Woontack Woo
KAIST, Korea

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 April 2015

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Research-article

Funding Sources

NSF
Army Research Laboratory
U.S. Defense Advanced Research Projects Agency

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CHI '15

Sponsor:

SIGCHI

CHI '15: CHI Conference on Human Factors in Computing Systems

April 18 - 23, 2015

Seoul, Republic of Korea

Acceptance Rates

CHI '15 Paper Acceptance Rate 486 of 2,120 submissions, 23%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Cited By

Han DZhu HChen WPan RLiu YZhou JFeng HZhang TWang XZhu MTao JFan CZhang X(2024)GraphFederator: Federated Visual Analysis for Multi-party Graphs2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00027(172-181)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00027
Wang SChen SCai TWu Y(2022)MULTI-NETVIS: Visual Analytics for Multivariate NetworkApplied Sciences10.3390/app1217840512:17(8405)Online publication date: 23-Aug-2022
https://doi.org/10.3390/app12178405
Ahn YYan MLin YChung WHwa R(2022)Tribe or Not? Critical Inspection of Group Differences Using TribalGramACM Transactions on Interactive Intelligent Systems10.1145/348450912:1(1-34)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3484509
Berger PSchumann HTominski C(2022)Integrating Visual Exploration and Direct Editing of Multivariate GraphsIntegrating Artificial Intelligence and Visualization for Visual Knowledge Discovery10.1007/978-3-030-93119-3_18(459-483)Online publication date: 5-Jun-2022
https://doi.org/10.1007/978-3-030-93119-3_18
Eichmann PEdge DEvans NLee BBrehmer MWhite C(2020)Orchard: Exploring Multivariate Heterogeneous Networks on Mobile PhonesComputer Graphics Forum10.1111/cgf.1396739:3(115-126)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.13967
McGee FGhoniem MMelançon GOtjacques BPinaud B(2019)The State of the Art in Multilayer Network VisualizationComputer Graphics Forum10.1111/cgf.1361038:6(125-149)Online publication date: 28-Mar-2019
https://doi.org/10.1111/cgf.13610
John MBaumann MSchuetz DKoch SErtl T(2019)A Visual Approach for the Comparative Analysis of Character Networks in Narrative Texts2019 IEEE Pacific Visualization Symposium (PacificVis)10.1109/PacificVis.2019.00037(247-256)Online publication date: Apr-2019
https://doi.org/10.1109/PacificVis.2019.00037
Bigelow AMonroe M(2019)Jacob's Ladder: The User Implications of Leveraging Graph Pivots2019 IEEE Pacific Visualization Symposium (PacificVis)10.1109/PacificVis.2019.00014(47-54)Online publication date: Apr-2019
https://doi.org/10.1109/PacificVis.2019.00014
Berger PSchumann HTominski C(2019)Visually Exploring Relations Between Structure and Attributes in Multivariate Graphs2019 23rd International Conference Information Visualisation (IV)10.1109/IV.2019.00051(261-268)Online publication date: Jul-2019
https://doi.org/10.1109/IV.2019.00051
Chen YGuan ZZhang RDu XWang Y(2019)A survey on visualization approaches for exploring association relationships in graph dataJournal of Visualization10.1007/s12650-019-00551-y22:3(625-639)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s12650-019-00551-y
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