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)
Cruciani, A., Pasquini, D., Amati, G., Vocca, P. (2019). About graph index compression techniques. In E.D.B. Maristella Agosti (a cura di), IIR 2019: 10th Italian Information Retrieval Workshop: proceedings of the 10th Italian Information Retrieval Workshop (pp. 21-24). Aachen : CEUR.
About graph index compression techniques
Antonio Cruciani;Daniele Pasquini;Giambattista Amati;Vocca P
2019-01-01
Abstract
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)File | Dimensione | Formato | |
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