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
Computing trusted authority scores in peer-to-peer web search networks
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
The Computational Complexity of Link Building
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Learning Representation and Control in Markov Decision Processes: New Frontiers
Foundations and Trends® in Machine Learning
An Arnoldi-Extrapolation algorithm for computing PageRank
Journal of Computational and Applied Mathematics
Parallel SimRank computation on large graphs with iterative aggregation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Leveraging community metadata for multimodal image ranking
Multimedia Tools and Applications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Proceedings of the 23rd international conference on World wide web
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An iterative algorithm based on aggregation/disaggregation principles is presented for updating the stationary distribution of a finite homogeneous irreducible Markov chain. The focus is on large-scale problems of the kind that are characterized by Google's PageRank application, but the algorithm is shown to work well in general contexts. The algorithm is flexible in that it allows for changes to the transition probabilities as well as for the creation or deletion of states. In addition to establishing the rate of convergence, it is proven that the algorithm is globally convergent. Results of numerical experiments are presented.