The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient crawling through URL ordering
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Does “authority” mean quality? predicting expert quality ratings of Web documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Internet Technology (TOIT)
Complexity measures and decision tree complexity: a survey
Theoretical Computer Science - Complexity and logic
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Local methods for estimating pagerank values
Proceedings of the thirteenth ACM international conference on Information and knowledge management
UbiCrawler: a scalable fully distributed web crawler
Software—Practice & Experience
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Semantic document engineering with WordNet and PageRank
Proceedings of the 2005 ACM symposium on Applied computing
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Information revelation and privacy in online social networks
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Google's PageRank and Beyond: The Science of Search Engine Rankings
Google's PageRank and Beyond: The Science of Search Engine Rankings
Computer Architecture, Fourth Edition: A Quantitative Approach
Computer Architecture, Fourth Edition: A Quantitative Approach
Toward alternative measures for ranking venues: a case of database research community
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
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
Top 10 algorithms in data mining
Knowledge and Information Systems
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Traps and Pitfalls of Topic-Biased PageRank
Algorithms and Models for the Web-Graph
Local approximation of pagerank and reverse pagerank
Proceedings of the 17th ACM conference on Information and knowledge management
Ordinal Ranking for Google's PageRank
SIAM Journal on Matrix Analysis and Applications
Choose the damping, choose the ranking?
Journal of Discrete Algorithms
ECIR'07 Proceedings of the 29th European conference on IR research
Ranking structural parameters for social networks
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
The power of local information in PageRank
Proceedings of the 22nd international conference on World Wide Web companion
A Local Method for ObjectRank Estimation
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Imagine you are a social network user who wants to search, in a list of potential candidates, for the best candidate for a job on the basis of their PageRank-induced importance ranking. Is it possible to compute this ranking for a low cost, by visiting only small subnetworks around the nodes that represent each candidate? The fundamental problem underpinning this question, i.e. computing locally the PageRank ranking of k nodes in an $n$-node graph, was first raised by Chen et al. (CIKM 2004) and then restated by Bar-Yossef and Mashiach (CIKM 2008). In this paper we formalize and provide the first analysis of the problem, proving that any local algorithm that computes a correct ranking must take into consideration Ω(√(kn)) nodes -- even when ranking the top $k$ nodes of the graph, even if their PageRank scores are "well separated", and even if the algorithm is randomized (and we prove a stronger Ω(n) bound for deterministic algorithms). Experiments carried out on large, publicly available crawls of the web and of a social network show that also in practice the fraction of the graph to be visited to compute the ranking may be considerable, both for algorithms that are always correct and for algorithms that employ (efficient) local score approximations.