Adaptive on-line page importance computation
WWW '03 Proceedings of the 12th international conference on World Wide Web
Implicit link analysis for small web search
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Local methods for estimating pagerank values
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Efficient PageRank approximation via graph aggregation
Information Retrieval
Estimating the global pagerank of web communities
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Local approximation of pagerank and reverse pagerank
Proceedings of the 17th ACM conference on Information and knowledge management
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Tag spam creates large non-giant connected components
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
ApproxRank: Estimating Rank for a Subgraph
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Topic level expertise search over heterogeneous networks
Machine Learning
TURank: twitter user ranking based on user-tweet graph analysis
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Local computation of PageRank: the ranking side
Proceedings of the 20th ACM international conference on Information and knowledge management
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ObjectRank is a method of link structure analysis to evaluate the importance of objects in a database. ObjectRank is known to be computationally expensive, because it requires iterative computations over a large graph. However, in many real applications, it is sufficient to compute the ObjectRank scores for only small fraction of objects. To address this problem, this paper proposes a novel method for estimating ObjectRank scores for specific objects by applying local computation over partial graphs, thereby allowing us to maintain low computational cost even for large graphs. Our basic idea is that, for a given target node, we induce a local graph by checking the edge weights and pruning the edges with considering their weights. We conduct experiments to compare our method with some comparative methods. The experimental results show that our method can reduce the computational cost while maintaining the accuracy.