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Frequent Pattern-based Graph Exploration

Published: 20 September 2019 Publication History

Abstract

Visual graph exploration can help the users have an intuitive impression about a dataset for the first time. However, it is better to provide some guidance so that the users can quickly locate the "interesting" or "informative" area in the graph. Then they can start the next stage of sense-making. We propose a visual exploration system which provides some frequent sub-graphs extracted from the whole graph, and the relationship between them. Thus, users can raise their study questions more quickly when they first confront a graph dataset. We evaluate our exploration system with two datasets. Specifically, we demonstrate how to raise a question and find the answer with our system, which validates the effectiveness of this project.

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  • (2021)Exemplar-based Layout Fine-tuning for Node-link DiagramsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303039327:2(1655-1665)Online publication date: Feb-2021

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cover image ACM Other conferences
VINCI '19: Proceedings of the 12th International Symposium on Visual Information Communication and Interaction
September 2019
201 pages
ISBN:9781450376266
DOI:10.1145/3356422
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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  • East China Normal University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2019

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Author Tags

  1. Visualization
  2. graph exploration
  3. graph mining
  4. graph visualization

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  • Refereed limited

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VINCI'2019

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Overall Acceptance Rate 71 of 193 submissions, 37%

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  • (2021)Exemplar-based Layout Fine-tuning for Node-link DiagramsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303039327:2(1655-1665)Online publication date: Feb-2021

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