By Topic

A Practical Method for Detecting Community Structures in Decentralized and Unstructured P2P Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Liu Meng ; State Key Lab. for Novel Software Technol., Nanjing Univ., Nanjing, China ; Liu Zhenxing ; Dou Wanchun

In decentralized and unstructured P2P networks like Gnutella, there is no coupling between topology and data location. Nodes can leave the network arbitrarily, which greatly affects their neighbors. We should determine in advance how many and which nodes will be affected when some nodes leave the network due to unstable Internet environment. In view of this challenge, we model the problem as detecting community structures, regarding the leaving nodes as community cores. Just as the flooding data queries in decentralized and unstructured P2P networks, the algorithm also works in a flooding way. It starts from the community cores and assign their neighbors to each corresponding community. Specifically, we focus on decentralized and unstructured P2P networks only. At last, a case study is presented for validating the method.

Published in:

Cloud and Green Computing (CGC), 2012 Second International Conference on

Date of Conference:

1-3 Nov. 2012