Title
Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model.
Abstract
Counting the number of times a pattern occurs in a database is a fundamental data mining problem. It is a subroutine in a diverse set of tasks ranging from pattern mining to supervised learning and probabilistic model learning. While a pattern and a database can take many forms, this paper focuses on the case where both the pattern and the database are graphs (networks). Unfortunately, in general, the problem of counting graph occurrences is #P-complete. In contrast to earlier work, which focused on exact counting for simple (i.e., very short) patterns, we present a sampling approach for estimating the statistics of larger graph pattern occurrences. We perform an empirical evaluation on synthetic and real-world data that validates the proposed algorithm, illustrates its practical behavior and provides insight into the trade-off between its accuracy of estimation and computational efficiency.
Year
DOI
Venue
2018
https://doi.org/10.1007/s10618-018-0553-2
Data Min. Knowl. Discov.
Keywords
Field
DocType
Graph sampling,Graph pattern matching,Parameter estimation,Statistical relational learning
Data mining,Subroutine,Computer science,Statistical relational learning,Supervised learning,Ranging,Sampling (statistics),Statistical model,Estimation theory,Relational model
Journal
Volume
Issue
ISSN
32
4
1384-5810
Citations 
PageRank 
References 
1
0.39
23
Authors
4
Name
Order
Citations
PageRank
Irma Ravkic191.56
Martin Znidarsic213311.51
Jan Ramon3144.84
Jesse Davis4183.47