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Graph Processing on FPGAs: Taxonomy, Survey, Challenges
Graph processing has become an important part of various areas, such as ...
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Data compression to choose a proper dynamic network representation
Dynamic network data are now available in a wide range of contexts and d...
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Universal Graph Compression: Stochastic Block Models
Motivated by the prevalent data science applications of processing and m...
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Demystifying Graph Databases: Analysis and Taxonomy of Data Organization, System Designs, and Graph Queries
Graph processing has become an important part of multiple areas of compu...
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Universal Lossless Compression of Graphical Data
Graphical data is comprised of a graph with marks on its edges and verti...
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Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics
We propose Slim Graph: the first programming model and framework for pra...
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Efficient Graph Compression Using Huffman Coding Based Techniques
Graphs have been extensively used to represent data from various domains...
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Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations
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.
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