Notice of Violation of IEEE Publication Principles: Efficient query routing in peer-to-peer networks

Notice of Violation of IEEE Publication Principles

"Efficient Query Routing in Peer-to-peer Networks,"
by L. Gatani, G. Lo Re, L. Noto,
in the Proceedings of the Third International Conference on Information Technology: Research and Education, 2005. ITRE 2005. pp. 393-397, 27-30 June 2005

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains substantial duplication of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

6S: "Distributing Crawling and Searching Across Web Peers"
By Filippo Menczer, Ruj Akavipat and Le-Shin Wu
available at the following URL: http://www.informatics.indiana.edu/research/publications/6S.pdf
In peer-to-peer (P2P) networks, efficient and scalable data retrieval represents a key problem. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of structured approaches. However, their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, in order to address this major limitation, we propose the adoption of a local adaptive routing protocol, that uses a smart neighbor selection process such that nodes with similar interest are likely to be grouped together as neighbors. Extensive simulations prove that the approach is able to dynamically adapt the topology to peer interests, organizing them into a small world network. The results also show that our algorithm has a very good impact on the successful rate, allowing to retrieve the resources searched even when they are sparse.