Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

TL;DR

This survey provides a comprehensive taxonomy of lossless graph compression techniques, analyzing their key ideas, formal foundations, and categorization across research areas, techniques, and features to guide optimal scheme selection.

Contribution

It is the first exhaustive survey and taxonomy of lossless graph compression, explaining key ideas and formal aspects, aiding in selecting suitable methods for specific applications.

Findings

Categorizes existing lossless graph compression schemes.

Explains key ideas and formal foundations of methods.

Provides a multi-dimensional taxonomy for scheme selection.

Abstract

Various graphs such as web or social networks may contain up to trillions of edges. Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses and the pressure on the memory subsystem. Yet, selecting a proper compression method is challenging as there exist a plethora of techniques, algorithms, domains, and approaches in compressing graphs. To facilitate this, we present a survey and taxonomy on lossless graph compression that is the first, to the best of our knowledge, to exhaustively analyze this domain. Moreover, our survey does not only categorize existing schemes, but also explains key ideas, discusses formal underpinning in selected works, and describes the space of the existing compression schemes using three dimensions: areas of research (e.g., compressing web graphs), techniques (e.g., gap encoding), and features (e.g., whether or not a given scheme targets dynamic graphs). Our survey can be used as a guide to select the best lossless compression scheme in a given setting.