Compressing Graph Data by Leveraging Domain Independent Knowledge
Graphs are used to solve many problems in the real world. At the same time size of the graphs presents a complex scenario to analyze essential information that they contain. Graph compression is used to understand high level structure of the graph through improved visualization. In this work, we int...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
LibraryPress@UF
2021-04-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| Subjects: | |
| Online Access: | https://journals.flvc.org/FLAIRS/article/view/128573 |
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| Summary: | Graphs are used to solve many problems in the real world.
At the same time size of the graphs presents a complex
scenario to analyze essential information that they contain.
Graph compression is used to understand high level structure
of the graph through improved visualization. In this work,
we introduce CRADLE (CompRessing grAph data with Domain
independent knowLEdge), a novel method based on
knowledge rule called netting, which reports the number of
external networks for each instance of the substructure. By
finding such substructures with more number of external networks
we can judiciously improve the compression rate. We
empirically evaluate our approach using synthetic as well as
real-world datasets. We compare CRADLE with baseline approaches.
Our proposed approach is comparable in compression
rate, search space, and runtimes to other well-known
graph mining approaches. |
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| ISSN: | 2334-0754 2334-0762 |