Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
Dynamic personalized pagerank in entity-relation graphs
Proceedings of the 16th international conference on World Wide Web
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Retrieving top-k prestige-based relevant spatial web objects
Proceedings of the VLDB Endowment
Index design and query processing for graph conductance search
The VLDB Journal — The International Journal on Very Large Data Bases
Fast and exact top-k search for random walk with restart
Proceedings of the VLDB Endowment
Relevance search in heterogeneous networks
Proceedings of the 15th International Conference on Extending Database Technology
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Density index and proximity search in large graphs
Proceedings of the 21st ACM international conference on Information and knowledge management
Efficient ad-hoc search for personalized PageRank
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
A proximity-based fallback model for hybrid web recommender systems
Proceedings of the 22nd international conference on World Wide Web companion
LR-PPR: locality-sensitive, re-use promoting, approximate personalized pagerank computation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Incremental and accuracy-aware personalized pagerank through scheduled approximation
Proceedings of the VLDB Endowment
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In entity-relation (ER) graphs (V,E), nodes V represent typed entities and edges E represent typed relations. For dynamic personalized PageRank queries, nodes are ranked by their steady-state probabilities obtained using the standard random surfer model. In this work, we propose a framework to answer top-k graph conductance queries. Our top-k ranking technique leads to a 4X speedup, and overall, our system executes queries 200-1600X faster than whole-graph PageRank. Some queries might contain hard predicates i.e. predicates that must be satisfied by the answer nodes. E.g. we may seek authoritative papers on public key cryptography, but only those written during 1997. We extend our system to handle hard predicates. Our system achieves these substantial query speedups while consuming only 10-20% of the space taken by a regular text index.