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Link Prediction in Complex Networks

Link Prediction in Complex Networks

Manisha Pujari, Rushed Kanawati
Copyright: © 2020 |Pages: 41
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch061
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MLA

Pujari, Manisha, and Rushed Kanawati. "Link Prediction in Complex Networks." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1196-1236. https://doi.org/10.4018/978-1-7998-2460-2.ch061

APA

Pujari, M. & Kanawati, R. (2020). Link Prediction in Complex Networks. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 1196-1236). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch061

Chicago

Pujari, Manisha, and Rushed Kanawati. "Link Prediction in Complex Networks." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1196-1236. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch061

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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.

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