The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Proceedings of the 11th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning to Create Customized Authority Lists
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
A new paradigm for ranking pages on the world wide web
WWW '03 Proceedings of the 12th international conference on World Wide Web
WWW '03 Proceedings of the 12th international conference on World Wide Web
XRANK: ranked keyword search over XML documents
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th 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
Journal of Artificial Intelligence Research
Learning web page scores by error back-propagation
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Shine: search heterogeneous interrelated entities
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to rank typed graph walks: local and global approaches
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Mining the search trails of surfing crowds: identifying relevant websites from user activity
Proceedings of the 17th international conference on World Wide Web
Learning to rank relational objects and its application to web search
Proceedings of the 17th international conference on World Wide Web
Fast mining of complex time-stamped events
Proceedings of the 17th ACM conference on Information and knowledge management
Summarization of social activity over time: people, actions and concepts in dynamic networks
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
iPoG: fast interactive proximity querying on graphs
Proceedings of the 18th ACM conference on Information and knowledge management
The graph neural network model
IEEE Transactions on Neural Networks
Preference learning with extreme examples
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ACM Transactions on Information Systems (TOIS)
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
Ranking authors in digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Investigation of the role of similarity measure and ranking algorithm in mining social networks
Journal of Information Science
An evolutionary PageRank approach for journal ranking with expert judgements
Journal of Information Science
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Business Intelligence and Analytics: Research Directions
ACM Transactions on Management Information Systems (TMIS)
Data-based research at IIT Bombay
ACM SIGMOD Record
QBEES: query by entity examples
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Entity ranking using click-log information
Intelligent Data Analysis
Hi-index | 0.00 |
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.