Elements of information theory
Elements of information theory
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
The Journal of Machine Learning Research
Learning structured prediction models: a large margin approach
Learning structured prediction models: a large margin approach
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
High accuracy retrieval with multiple nested ranker
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank networked entities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Disorder inequality: a combinatorial approach to nearest neighbor search
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Fast mining of complex time-stamped events
Proceedings of the 17th ACM conference on Information and knowledge management
Effective latent space graph-based re-ranking model with global consistency
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Semi-supervised ensemble ranking
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Combinatorial Framework for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Mining Query Logs: Turning Search Usage Data into Knowledge
Foundations and Trends in Information Retrieval
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Ranking on large-scale graphs with rich metadata
Proceedings of the 20th international conference companion on World wide web
Diversity in ranking via resistive graph centers
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
SISP: a new framework for searching the informative subgraph based on PSO
Proceedings of the 20th ACM international conference on Information and knowledge management
Large-scale graph mining and learning for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Automatic seed set expansion for trust propagation based anti-spam algorithms
Information Sciences: an International Journal
Let's get together: the formation and success of online creative collaborations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data-based research at IIT Bombay
ACM SIGMOD Record
Homophily, popularity and randomness: modelling growth of online social network
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An agent-based model of the development of friendship links within Facebook
Computational & Mathematical Organization Theory
Combining prestige and relevance ranking for personalized recommendation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalization guarantees. In contrast, Page-rank variants are based on Markovian random walks. For directed graphs, there is no simple known correspondence between these views of scoring/ranking. Recent scalable algorithms for learning the Pagerank transition probabilities do not have generalization guarantees. In this paper we show some correspondence results between the Laplacian and the Pagerank approaches, and give new generalization guarantees for the latter. We enhance the Pagerank-learning approaches to use an additive margin. We also propose a general framework for rank-sensitive score-learning, and apply it to Laplacian smoothing. Experimental results are promising.