Local methods for estimating pagerank values
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Reducing the history in decentralized interaction-based reputation systems
IFIP'12 Proceedings of the 11th international IFIP TC 6 conference on Networking - Volume Part II
The power of local information in PageRank
Proceedings of the 22nd international conference on World Wide Web companion
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We consider the problem of approximating the PageRank of a target node using only local information provided by a link server. We prove that local approximation of PageRank is feasible if and only if the graph has low in-degree and admits fast PageRank convergence. While natural graphs, such as the web graph, are abundant with high in-degree nodes, making local PageRank approximation too costly, we show that reverse natural graphs tend to have low indegree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageRank locally. Finally, we demonstrate the usefulness of Reverse PageRank in five different applications.