Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Learning to Create Customized Authority Lists
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
WWW '03 Proceedings of the 12th international conference on World Wide Web
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning to rank networked entities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Link analysis using time series of web graphs
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
BrowseRank: letting web users vote for page importance
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A general markov framework for page importance computation
Proceedings of the 18th ACM conference on Information and knowledge management
Ranking and semi-supervised classification on large scale graphs using map-reduce
TextGraphs-4 Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing
Learning parameters in entity relationship graphs from ranking preferences
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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For many Web applications, one needs to deal with the ranking problem on large-scale graphs with rich metadata. However, it is non-trivial to perform efficient and effective ranking on them. On one aspect, we need to design scalable algorithms. On another aspect, we also need to develop powerful computational infrastructure to support these algorithms. This tutorial aims at giving a timely introduction to the promising advances in the aforementioned aspects in recent years, and providing the audiences with a comprehensive view on the related literature.