About Graph Index Compression Techniques

We perform a preliminary study on large graph efficient indexing
using a gap-based compression techniques and different node labelling
functions. As baseline we use the Webgraph + LLP labelling
function. To index the graph we use three labelling functions: Pagerank,
HITS, and Pagerank with random walks choosing restart nodes
with HITS authority scores. To compress the graphs we use Varint
GB, with and without d-gaps, derived by rank value of the labelling
function. Overall, we compare 8 different methods on different
datasets composed by the WebGraph eu-2005, uk-2007-05@100000,
cnr-2000, and the social networks, enron, ljournal-2008, provided
by the Laboratory for Web Algorithmics (LAW).