A Survey on Methods and Systems for Graph Compression

Abstract

We present an informal survey (meant to accompany another paper) on graph compression methods. We focus on lossless methods, briefly list available pproaches, and compare them where possible or give some indicators on their compression ratios. We also mention some relevant results from the field of lossy compression and algorithms specialized for the use on large graphs. --- Note: The comparison is by no means complete. This document is a first draft and will be updated and extended.

References in corpus (5)

• GraphLab: A New Framework For Parallel Machine Learning
• DAGGER: A Scalable Index for Reachability Queries in Large Dynamic Graphs
• GraphChi-DB: Simple Design for a Scalable Graph Database System -- on Just a PC
• Scalable RDF Data Compression using X10
• PReaCH: A Fast Lightweight Reachability Index using Pruning and Contraction Hierarchies

Cited by in corpus (6)

• Compressing Graphs and Indexes with Recursive Graph Bisection
• Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations
• Extracting and Analyzing Hidden Graphs from Relational Databases
• Impossibility Results for Grammar-Compressed Linear Algebra
• Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
• BernoulliZip: a Compression Algorithm for Bernoulli Processes and Erdos-Renyi Graphs