Summarizing and understanding large graphs

DOI10.1002/sam.11267OpenAlexW2199530723WikidataQ125131127 ScholiaQ125131127MaRDI QIDQ4970005

U. Kang, Christos Faloutsos, Jilles Vreeken, Danai Koutra

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1406.3411

zbMATH Keywords

minimum description lengthgraph visualizationgraph summarization

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (5)

Online summarization of dynamic graphs using subjective interestingness for sequential data ⋮ Set-based approximate approach for lossless graph summarization ⋮ Large-scale network motif analysis using compression ⋮ Minimum description length revisited ⋮ The minimum description length principle for pattern mining: a survey

Uses Software

• MapReduce
• gSpan
• PEGASUS
• MDL4BMF
• Krimp
• WebGraph
• OddBall

Cites Work

• Unnamed Item
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• Krimp: mining itemsets that compress
• Structure in the Enron email dataset
• A universal prior for integers and estimation by minimum description length
• Modeling by shortest data description
• Relaxing the uniformity and independence assumptions using the concept of fractal dimension
• Graph compression by BFS
• An algorithm for drawing general undirected graphs
• Comparing apples and oranges: measuring differences between exploratory data mining results
• Clustering by Compression
• Parallel Multilevel series k-Way Partitioning Scheme for Irregular Graphs
• Enriching Taxonomies of Place Types Using Flickr
• Elements of Information Theory

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