IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Link Prediction in Complex Networks

Link Prediction in Complex Networks
View Sample PDF
Author(s): Manisha Pujari (Université Paris 13, France)and Rushed Kanawati (Université Paris 13, France)
Copyright: 2020
Pages: 41
Source title: Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch061

Purchase

View Link Prediction in Complex Networks on the publisher's website for pricing and purchasing information.

Abstract

This chapter presents the problem of link prediction in complex networks. It provides general description, formal definition of the problem and applications. It gives a state-of-art of various existing link prediction approaches concentrating more on topological approaches. It presents the main challenges of link prediction task in real networks. There is description of our new link prediction approach based on supervised rank aggregation and our attempts to deal with two of the challenges to improve the prediction results. One approach is to extend the set of attributes describing an example (pair of nodes) calculated in a multiplex network that includes the target network. Multiplex networks have a layered structure, each layer having different kinds of links between same sets of nodes. The second way is to use community information for sampling of examples to deal with the problem of class imbalance. All experiments have been conducted on real networks extracted from well-known DBLP bibliographic database.

Related Content

Jaime Salvador, Zoila Ruiz, Jose Garcia-Rodriguez. © 2020. 12 pages.
Stavros Pitoglou. © 2020. 11 pages.
Mette L. Baran. © 2020. 13 pages.
Yingxu Wang, Victor Raskin, Julia M. Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, Newton Howard. © 2020. 15 pages.
Yingxu Wang, Lotfi A. Zadeh, Bernard Widrow, Newton Howard, Françoise Beaufays, George Baciu, D. Frank Hsu, Guiming Luo, Fumio Mizoguchi, Shushma Patel, Victor Raskin, Shusaku Tsumoto, Wei Wei, Du Zhang. © 2020. 18 pages.
Nayem Rahman. © 2020. 24 pages.
Amir Manzoor. © 2020. 27 pages.
Body Bottom