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We propose Slim Graph: the first programming model and framework forpractical lossy graph compression that facilitates high-performance approximategraph processing, storage, and analytics. Slim Graph enables the developer toexpress numerous compression schemes using small and programmable compressionkernels that can access and modify local parts of input graphs. Such kernelsare executed in parallel by the underlying engine, isolating developers fromcomplexities of parallel programming. Our kernels implement novel graphcompression schemes that preserve numerous graph properties, for exampleconnected components, minimum spanning trees, or graph spectra. Finally, SlimGraph uses statistical divergences and other metrics to analyze the accuracy oflossy graph compression. We illustrate both theoretically and empirically thatSlim Graph accelerates numerous graph algorithms, reduces storage used by graphdatasets, and ensures high accuracy of results. Slim Graph may become thecommon ground for developing, executing, and analyzing emerging lossy graphcompression schemes.

Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics