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Visualizing Attributed Graphs via Terrain Metaphor

Authors:

Srinivasan ParthasarathyAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1325 - 1334

https://doi.org/10.1145/3097983.3098130

Published: 04 August 2017 Publication History

Abstract

The value proposition of a dataset often resides in the implicit interconnections or explicit relationships (patterns) among individual entities, and is often modeled as a graph. Effective visualization of such graphs can lead to key insights uncovering such value. In this article we propose a visualization method to explore attributed graphs with numerical attributes associated with nodes (or edges). Such numerical attributes can represent raw content information, similarities, or derived information reflecting important network measures such as triangle density and centrality. The proposed visualization strategy seeks to simultaneously uncover the relationship between attribute values and graph topology, and relies on transforming the network to generate a terrain map. A key objective here is to ensure that the terrain map reveals the overall distribution of components-of-interest (e.g. dense subgraphs, k-cores) and the relationships among them while being sensitive to the attribute values over the graph. We also design extensions that can capture the relationship across multiple numerical attributes. We demonstrate the efficacy of our method on several real-world data science tasks while scaling to large graphs with millions of nodes.

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Cited By

Di Battista GGrosso FMontorselli SPatrignani M(2024)Treebar Maps: Schematic Representation of Networks at Scale2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00023(132-141)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00023
Abello JZhang HNakhimovich DHan CAanjaneya M(2022)Giga Graph Cities: Their Buckets, Buildings, Waves, and FragmentsIEEE Computer Graphics and Applications10.1109/MCG.2022.317265042:3(53-64)Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1109/MCG.2022.3172650
Evers MHerick MMolchanov VLinsen L(2022)Coherent Topological Landscapes for Simulation EnsemblesComputer Vision, Imaging and Computer Graphics Theory and Applications10.1007/978-3-030-94893-1_10(223-237)Online publication date: 22-Jan-2022
https://doi.org/10.1007/978-3-030-94893-1_10
Show More Cited By

Index Terms

Visualizing Attributed Graphs via Terrain Metaphor
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Information visualization
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Publication History

Published: 04 August 2017

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KDD '17

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KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Di Battista GGrosso FMontorselli SPatrignani M(2024)Treebar Maps: Schematic Representation of Networks at Scale2024 IEEE 17th Pacific Visualization Conference (PacificVis)10.1109/PacificVis60374.2024.00023(132-141)Online publication date: 23-Apr-2024
https://doi.org/10.1109/PacificVis60374.2024.00023
Abello JZhang HNakhimovich DHan CAanjaneya M(2022)Giga Graph Cities: Their Buckets, Buildings, Waves, and FragmentsIEEE Computer Graphics and Applications10.1109/MCG.2022.317265042:3(53-64)Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1109/MCG.2022.3172650
Evers MHerick MMolchanov VLinsen L(2022)Coherent Topological Landscapes for Simulation EnsemblesComputer Vision, Imaging and Computer Graphics Theory and Applications10.1007/978-3-030-94893-1_10(223-237)Online publication date: 22-Jan-2022
https://doi.org/10.1007/978-3-030-94893-1_10
Preiner RSchmidt JKrösl KSchreck TMistelbauer G(2020)Augmenting Node‐Link Diagrams with Topographic Attribute MapsComputer Graphics Forum10.1111/cgf.1398739:3(369-381)Online publication date: 18-Jul-2020
https://doi.org/10.1111/cgf.13987
Dharamsotu BRani KAbdul Moiz SRao C(2019)k-NN Sampling for Visualization of Dynamic Data Using LION-tSNE2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC)10.1109/HiPC.2019.00019(63-72)Online publication date: Dec-2019
https://doi.org/10.1109/HiPC.2019.00019
Yoghourdjian VDwyer TKlein KMarriott KWybrow M(2018)Graph Thumbnails: Identifying and Comparing Multiple Graphs at a GlanceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.279096124:12(3081-3095)Online publication date: 1-Dec-2018
https://doi.org/10.1109/TVCG.2018.2790961

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