Compressing Graph Data by Leveraging Domain Independent Knowledge

Authors

  • Dr. Sirisha Velampalli Assistant Professor

DOI:

https://doi.org/10.32473/flairs.v34i1.128573

Keywords:

Graph Compression, Domain Independent Knowledge, Knowledge Rule, Visualization

Abstract

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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Published

2021-04-18

How to Cite

Velampalli, D. S. (2021). Compressing Graph Data by Leveraging Domain Independent Knowledge. The International FLAIRS Conference Proceedings, 34. https://doi.org/10.32473/flairs.v34i1.128573

Issue

Section

Special Track: Neural Networks and Data Mining