Paper Info
Title
Regular Decomposition Of Large Graphs And Other Structures: Scalability And Robustness Towards Missing Data
Abstract
A method for compression of large graphs and matrices to a block structure is further developed. Szemeredi's regularity lemma is used as a generic motivation of the significance of stochastic block models. Another ingredient of the method is Rissanen's minimum description length principle (MDL). We continue our previous work on the subject, considering cases of missing data and scaling of algorithms to extremely large size of graphs. In this way it would be possible to find out a large scale structure of a huge graphs of certain type using only a tiny part of graph information and obtaining a compact representation of such graphs useful in computations and visualization.
Year
DOI
Venue
2017
10.1109/BigData.2017.8258320
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Field
DocType
ISSN
Data mining,Computer science,Matrix decomposition,Minimum description length,Theoretical computer science,Robustness (computer science),Power graph analysis,Missing data,Sparse matrix,Lemma (mathematics),Scalability
Conference
2639-1589
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
H. Reittu1467.16
Ilkka Norros261386.52