Large-scale network motif analysis using compression

DOI10.1007/s10618-020-00691-yzbMath1455.68134arXiv1701.02026OpenAlexW2945735730WikidataQ111522720 ScholiaQ111522720MaRDI QIDQ2212516

Steven de Rooij, Peter Bloem

Publication date: 23 November 2020

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.02026

zbMATH Keywords

minimum description lengthnetwork analysismotifs

Mathematics Subject Classification ID

Small world graphs, complex networks (graph-theoretic aspects) (05C82) Graph theory (including graph drawing) in computer science (68R10) Computational aspects of data analysis and big data (68T09)

Related Items (1)

The minimum description length principle for pattern mining: a survey

Uses Software

• KONECT
• DBpedia
• Krimp

Cites Work

• Unnamed Item
• Unnamed Item
• Subjective interestingness of subgraph patterns
• Krimp: mining itemsets that compress
• Modeling by shortest data description
• The asymptotic number of labeled graphs with given degree sequences
• Subjectively interesting connecting trees and forests
• A Sequential Importance Sampling Algorithm for Generating Random Graphs with Prescribed Degrees
• Statistical mechanics of complex networks
• The performance of universal encoding
• Arithmetic Coding
• Almost the Best of Three Worlds: Risk, Consistency and Optional Stopping for the Switch Criterion in Nested Model Selection
• Summarizing and understanding large graphs
• Frequent Pattern Mining
• Elements of Information Theory
• Random Graphs
• Sequential Minimax Search for a Maximum
• Networks

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