.png){kind=icon}
Return
Sampling informative patterns from large single networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2020.01.042.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The set of all frequent patterns that are extracted from a single network can be huge. A technique recently proposed for obtaining a compact, informative and useful set of patterns is output sampling, where a small set of frequent patterns is randomly chosen. However, existing output sampling algorithms work only in the transactional setting, where the database consists of a collection of relatively small graphs. In this paper, first we extend the output sampling framework to the single network setting where the database is a large single graph, counting supports of patterns is more complicated, and frequent patterns might be sampled based on any arbitrary target distribution. Then, we propose sampling techniques that are based on more interesting/informative measures or those that are specific to large single networks, such as product of the pattern size with its support, network compressibility, and pattern density. Finally, we study the empirical behavior of our algorithm in a real-world case study. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
World Wide Web
Large single network mining
Social network analysis
Informative patterns
Frequent patterns
Output sampling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
6.1
Papers:
6.9K
Citations:
2.3W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveLower Extremity Necrotizing Fasciitis in Diabetic and Nondiabetic Patients: Mortality and Amputation
PREAI
ShareSaveShareSave
View more