Paper Info
Title
The Geometric Block Model and Applications.
Abstract
This is a note accompanying an invited talk at the Allerton conference where we summarize our results related to geometric block model (GBM), a random graph model for communities in networks that is based on geometric graphs. The GBM is distinguished from many other community models because of correlated edge formation, which makes GBM less random in nature, but more complicated to analyze. On the algorithmic side, we describe a simple triangle-counting process that performs sequential edge removal from the graph to reveal the communities. The algorithm critically uses the connectivity properties of annulus graphs or vertex-random graphs.
Year
DOI
Venue
2018
10.1109/ALLERTON.2018.8635938
2018 56TH ANNUAL ALLERTON CONFERENCE ON COMMUNICATION, CONTROL, AND COMPUTING (ALLERTON)
Field
DocType
ISSN
Graph,Random variable,Mathematical optimization,Random graph,Computer science,Theoretical computer science,Cluster analysis,Artificial neural network
Conference
2474-0195
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Sainyam Galhotra110710.96
Soumyabrata Pal223.76
Arya Mazumdar330741.81
Barna Saha462637.56