Peer-to-Peer Networks for Self-Organizing Virtual Communities

Project Description
Project Publications
Project Software
Related Work
Background Reading


Project Description

This project extends peer-to-peer communication networks to better support formation of virtual communities in wide area computer networks. Virtual communities bring together individuals with similar interests, but the difficulty of forming them and sustaining critical mass discourages communities that serve small populations or compete with existing communities. Large-scale peer-to-peer networks offer the possibility of self-organizing communities, in which nodes recognize and create relatively stable connections to other nodes with similar interests.

The solution includes nodes that learn about their network neighborhoods, nodes that offer partial (and competing) directory services, new methods of routing messages efficiently in peer-to-peer networks, more accurate methods of making resource selection decisions in environments containing many resources, and a utility-theoretic model for decision-making by individual nodes that incorporate multiple task requirements (e.g., cost, accuracy, and reliability).

The scientific results are more robust and efficient peer-to-peer networks, new techniques for forming virtual communities, and a better understanding of how complex peer-to-peer networks work. A software simulator enables CS, MIS, and Business students to study virtual communities, for example testing hypotheses about why marketplaces fail or policies that encourage community formation. The basic science can be used to build search tools that explicitly consider tens of thousands of databases, software that supports dynamic creation of virtual communities within organizational intranets in response to unforseen developments (e.g., the DoD), and wireless networks in which devices work whenever they are in range of another device.


Faculty Grad Students Undergrads
Jamie Callan
Ramayya Krishnan
Alan Montgomery
Mike Smith
Rahul Telang
Atip Asvanund
Sarvesh Bagla
Brett Gordon
Munjal Kapadia
Jie Lu
Stanley Ouyang
M. Elena Renda
Luo Si
Xin Wang
Shion Deysaskas

Project Publications

Project Software

Gnutella 0.4 simulator
Gnutella 0.6 simulator
INI Thesis
INI Thesis Code

Related Work Tools

Background Reading

Sponsored by
National Science Foundation
The National Science Foundation

Updated on June 25, 2003.