Skip to main content

A graph compression algorithm for large-scale web-like graphs (web/social networks/citation graphs)

Project description

An efficient graph compression algorithm for large-scale graphs that exploits the graph’s structure to achieve better compression rate. In particular, it makes use of the locality of reference in the graph and the power law distribution of its nodes’ degrees, two properties usually observed in large sparse graphs that model networks created by human activity (eg. the web, social networks or citation graphs). Furthermore, this approach focuses on navigating through both the incoming and outgoing edges of each node of the compressed graph in linear time.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for SiVaC, version 0.2
Filename, size File type Python version Upload date Hashes
Filename, size SiVaCv0.2.tar.gz (2.0 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page