Approximate counting, uniform generation and rapidly mixing Markov chains
Information and Computation
Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
Lower bounds for sampling algorithms for estimating the average
Information Processing Letters
Efficient crawling through URL ordering
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The connectivity server: fast access to linkage information on the Web
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Finding related pages in the World Wide Web
WWW '99 Proceedings of the eighth international conference on World Wide Web
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Sampling algorithms: lower bounds and applications
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
ACM Transactions on Internet Technology (TOIT)
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
Computer
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Local methods for estimating pagerank values
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Efficient PageRank approximation via graph aggregation
Information Retrieval
Estimating the global pagerank of web communities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient and decentralized PageRank approximation in a peer-to-peer web search network
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
The discoverability of the web
Proceedings of the 16th international conference on World Wide Web
Decoding the structure of the WWW: A comparative analysis of Web crawls
ACM Transactions on the Web (TWEB)
Computing pagerank in a distributed internet search system
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient parallel computation of pagerank
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
AnchorWoman: top-k structured mobile web search engine
Proceedings of the 18th ACM conference on Information and knowledge management
Retrieving top-k prestige-based relevant spatial web objects
Proceedings of the VLDB Endowment
Local computation of PageRank: the ranking side
Proceedings of the 20th ACM international conference on Information and knowledge management
DIGRank: using global degree to facilitate ranking in an incomplete graph
Proceedings of the 20th ACM international conference on Information and knowledge management
A Local Method for ObjectRank Estimation
Proceedings of International Conference on Information Integration and Web-based Applications & Services
On the embeddability of random walk distances
Proceedings of the VLDB Endowment
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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. This problem was originally studied by Chen, Gan, and Suel (CIKM 2004), who presented an algorithm for tackling it. We prove that local approximation of PageRank, even to within modest approximation factors, is infeasible in the worst-case, as it requires probing the link server for Ω(n) nodes, where n is the size of the graph. The difficulty emanates from nodes of high in-degree and/or from slow convergence of the PageRank random walk. We show that when the graph has bounded in-degree and admits fast PageRank convergence, then local PageRank approximation can be done using a small number of queries. Unfortunately, natural graphs, such as the web graph, are abundant with high in-degree nodes, making this algorithm (or any other local approximation algorithm) too costly. On the other hand, reverse natural graphs tend to have low in-degree while maintaining fast PageRank convergence. It follows that calculating Reverse PageRank locally is frequently more feasible than computing PageRank locally. We demonstrate that Reverse PageRank is useful for several applications, including computation of hub scores for web pages, finding influencers in social networks, obtaining good seeds for crawling, and measurement of semantic relatedness between concepts in a taxonomy.