Paper Info
Title
Keynote 5: Exploiting social metrics in content distribution
Abstract
Summary form only given. Social metrics have recently been considered to capture the degree of similarity in interests of the nodes as well as their “standing” within a community or network. In this talk some recent works-examples are briefly presented showing the potential benefits from incorporating social metrics in content replication, forwarding and placement. More specifically, a framework for assessing interest similarity is presented and applied to illustrate how similarity affects the effectiveness of content replication and forwarding. In addition, the widely adopted Betweenness Centrality metric is revisited and issues associated with its computation and appropriateness for content forwarding are discussed. Then, modifications and easily computable variants are introduced and their effectiveness is illustrated.
Year
DOI
Venue
2011
10.1109/ISCC.2011.5984770
ISCC
Field
DocType
ISSN
Degree of similarity,Content replication,Computer science,Theoretical computer science,Betweenness centrality,Computation
Conference
1530-1346 E-ISBN : 978-1-4577-0678-3
ISBN
Citations 
PageRank 
978-1-4577-0678-3
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
I. Stavrakakis1568.94