Efficient partial-pairs simrank search on large networks
- Submitting institution
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The University of Warwick
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12488
- Type
- D - Journal article
- DOI
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10.14778/2735479.2735489
- Title of journal
- Proceedings of the VLDB Endowment
- Article number
- -
- First page
- 569
- Volume
- 8
- Issue
- 5
- ISSN
- 2150-8097
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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D - Data Science, Systems and Security
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published at one of the two top conferences in data management, and supported by the EU FP7 WISDOM project and the NEC Corporation, this research devised a novel scalable similarity-join algorithm that retrieves similar pairs of objects from two collections of nodes based on graph proximity in linear time without loss of accuracy. It led to several subsequent works by international groups on scale-free bipartite networks (Rahul Agrawal, Microsoft) and heterogeneous social graphs (Jennifer Widom, Stanford). It has also impacted research on e.g. information extraction and knowledge harvesting (Gerhard Weikum, MPI), and random graph data management (Xiaokui Xiao, NUS).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -