Community Discovery of P2P Resources Based on Bipartite Graph

Author

Li Jin ; Zhou Zhu-rong

Author_Institution

Coll. of Comput. & Inf. Sci., Southwest Univ., Chongqing, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Finding related resources is important for assisting resource retrieval and recommendation in the P2P network. This paper proposes a method for discovering such clusters of related resources, which are called resource communities. Graph mining approach is applicable here. Discovering communities is based on bipartite graph which is composed of resources and keywords. The data is extracted from the analysis of users´ search and download behavior. Experiment shows that this method is effective in discovering resource communities and improving the efficiency of resource retrieval.

Keywords

data mining; graph theory; information retrieval; peer-to-peer computing; P2P resources; bipartite graph; community discovery; graph mining approach; resource communities; resource recommendation; resource retrieval; Bipartite graph; Computer networks; Costs; Data mining; Educational institutions; Humans; Information retrieval; Information science; Optimization methods; Peer to peer computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5365800

Filename

5365800