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» Speeding up algorithms on compressed web graphs
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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
DCC
2001
IEEE
14 years 7 months ago
Towards Compressing Web Graphs
We consider the problem of compressing graphs of the link structure of the World Wide Web. We provide efficient algorithms for such compression that are motivated by recently prop...
Micah Adler, Michael Mitzenmacher
WWW
2010
ACM
14 years 2 months ago
Matrix "Bit" loaded: a scalable lightweight join query processor for RDF data
The Semantic Web community, until now, has used traditional database systems for the storage and querying of RDF data. The SPARQL query language also closely follows SQL syntax. A...
Medha Atre, Vineet Chaoji, Mohammed J. Zaki, James...
WWW
2004
ACM
14 years 8 months ago
The webgraph framework I: compression techniques
Studying Web graphs is often difficult due to their large size. Recently, several proposals have been published about various techniques that allow to store a Web graph in memory ...
Paolo Boldi, Sebastiano Vigna
IM
2007
13 years 7 months ago
Approximating Personalized PageRank with Minimal Use of Web Graph Data
Abstract. In this paper, we consider the problem of calculating fast and accurate approximations to the personalized PageRank score of a webpage. We focus on techniques to improve ...
David Gleich, Marzia Polito