Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs
Nowadays there is a great interest in the visualization of property graphs to make their navigation, inspection, and visual analysis easier. However, property graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. With...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10256185/ |
| _version_ | 1827792863343149056 |
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| author | Marco Mesiti Mario Pennacchioni Paolo Perlasca |
| author_facet | Marco Mesiti Mario Pennacchioni Paolo Perlasca |
| author_sort | Marco Mesiti |
| collection | DOAJ |
| description | Nowadays there is a great interest in the visualization of property graphs to make their navigation, inspection, and visual analysis easier. However, property graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. With the aim of reducing the size of the visualized graph, several approaches have been proposed for substituting clusters of related vertices with aggregated meta-nodes and introducing meta-edges among them, but they usually consider the graph in main-memory and do not adopt efficient data structures for extracting parts of it from the disk. The purpose of this paper is to optimize the preparation of the graph to be visualized according to a certain resolution level by introducing refined data structures and specifically tailored algorithms. By means of them, the rendering time is reduced when changing the current visualization through zoom-in, zoom-out, and related operations. Starting from a cluster hierarchy that represents the possible aggregations of graph nodes, in the paper we characterize a visualization according to a horizontal slice of the hierarchy and propose indexing structures and incremental algorithms for quickly passing to a new visualization with minimal changes of the current one. In this process, we ensure a consistent and efficient aggregation of addictive properties associated with nodes and edges. An extensive experimental analysis has been conducted to assess the quality of the proposed solution. |
| first_indexed | 2024-03-11T18:08:17Z |
| format | Article |
| id | doaj.art-6d6bc09bfa904f17af5965cac547f036 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:08:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6d6bc09bfa904f17af5965cac547f0362023-10-16T23:00:45ZengIEEEIEEE Access2169-35362023-01-011110358510360010.1109/ACCESS.2023.331736910256185Indexing Structures for the Efficient Multi-Resolution Visualization of Big GraphsMarco Mesiti0https://orcid.org/0000-0001-5701-0080Mario Pennacchioni1Paolo Perlasca2https://orcid.org/0000-0001-6674-2822Department of Computer Science, Università di Milano, Milan, ItalyDepartment of Computer Science, Università di Milano, Milan, ItalyDepartment of Computer Science, Università di Milano, Milan, ItalyNowadays there is a great interest in the visualization of property graphs to make their navigation, inspection, and visual analysis easier. However, property graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. With the aim of reducing the size of the visualized graph, several approaches have been proposed for substituting clusters of related vertices with aggregated meta-nodes and introducing meta-edges among them, but they usually consider the graph in main-memory and do not adopt efficient data structures for extracting parts of it from the disk. The purpose of this paper is to optimize the preparation of the graph to be visualized according to a certain resolution level by introducing refined data structures and specifically tailored algorithms. By means of them, the rendering time is reduced when changing the current visualization through zoom-in, zoom-out, and related operations. Starting from a cluster hierarchy that represents the possible aggregations of graph nodes, in the paper we characterize a visualization according to a horizontal slice of the hierarchy and propose indexing structures and incremental algorithms for quickly passing to a new visualization with minimal changes of the current one. In this process, we ensure a consistent and efficient aggregation of addictive properties associated with nodes and edges. An extensive experimental analysis has been conducted to assess the quality of the proposed solution.https://ieeexplore.ieee.org/document/10256185/Property graphsnode indicesedge indicesaggregations according to a cluster hierarchymulti-resolution visualizationzoom-in and zoom-out operations |
| spellingShingle | Marco Mesiti Mario Pennacchioni Paolo Perlasca Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs IEEE Access Property graphs node indices edge indices aggregations according to a cluster hierarchy multi-resolution visualization zoom-in and zoom-out operations |
| title | Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs |
| title_full | Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs |
| title_fullStr | Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs |
| title_full_unstemmed | Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs |
| title_short | Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs |
| title_sort | indexing structures for the efficient multi resolution visualization of big graphs |
| topic | Property graphs node indices edge indices aggregations according to a cluster hierarchy multi-resolution visualization zoom-in and zoom-out operations |
| url | https://ieeexplore.ieee.org/document/10256185/ |
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