Association Rules with Graph Patterns
- Submitting institution
-
University of Edinburgh
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 59512542
- Type
- E - Conference contribution
- DOI
-
10.14778/2824032.2824048
- Title of conference / published proceedings
- Proceedings of the VLDB Endowment (PVLDB)
- First page
- 1502
- Volume
- 8
- Issue
- 12
- ISSN
- 2150-8097
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- 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
-
3
- Research group(s)
-
C - Foundations of Computation
- Citation count
- 30
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proposes graph-pattern association rules, referred to as GPARs. Extending association rules for itemsets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We develop effective parallel algorithms for (a) discovering useful GPARS, and (b) identifying potential customers when provided a set of GPARs. This provides a package of techniques for social media marketing, which is predicted to trump traditional marketing. As an indication of the practical impact, Huawei Technologies has acquired the patentable idea of the paper, to improve their recommendation system on mobile-cloud platform.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -