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Journal of Visualization

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02-04-2019 | Regular Paper

A survey on visualization approaches for exploring association relationships in graph data

Authors: Yi Chen, Zeli Guan, Rong Zhang, Xiaomin Du, Yunhai Wang

Published in: Journal of Visualization | Issue 3/2019

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Abstract

Exploring relationships in complex datasets is one of the challenges in today’s big data era. The graph-based visualization approach, which integrates the advantages of graph analysis theory and visualization technologies and combines machine and human intelligence, has become an effective means for analyzing various relationships in complex datasets. In this paper, we first introduce a graph-based visual analytics model for associated data. Then, we summarize seven typical visualization methods for associated data according to their layout features, including their node-link diagram, adjacency matrix, hypergraph, flow diagram, graphs with geospatial information, multi-attribute graph, and space-filling diagram and discuss their advantages and disadvantages. We describe current graph simplification and interaction techniques, including graph filtering, node clustering, edge bundling, graph data dimension reduction, and topology-based graph transformation. Finally, we discuss the potential challenges and developmental trends of the research direction.

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Title: A survey on visualization approaches for exploring association relationships in graph data
Authors: Yi Chen
Zeli Guan
Rong Zhang
Xiaomin Du
Yunhai Wang
Publication date: 02-04-2019
Publisher: Springer Berlin Heidelberg
Published in: Journal of Visualization / Issue 3/2019
Print ISSN: 1343-8875
Electronic ISSN: 1875-8975
DOI: https://doi.org/10.1007/s12650-019-00551-y

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