Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Sarmento, RP (Author) Other; Cordeiro, M (Author) Other; Pavel Brazdil (Author) FEP; João Gama (Author) FEP

Conference proceedings International

Title: Complex Networks & Their Applications VI - Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications), COMPLEX NETWORKS 2017, Lyon, France, November 29 - December 1, 2017.

Pages: 341-352

6th International Conference on Complex Networks and Their Applications, Complex Networks 2017

29 November 2017 through 1 December 2017

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00N-9PB

DOI: 10.1007/978-3-319-72150-7_28

Abstract (EN): Social Network Analysis (SNA) is an important research area. It originated in sociology but has spread to other areas of research, including anthropology, biology, information science, organizational studies, political science, and computer science. This has stimulated research on how to support SNA with the development of new algorithms. One of the critical areas involves calculation of different centrality measures. The challenge is how to do this fast, as many increasingly larger datasets are available. Our contribution is an incremental version of the Laplacian Centrality measure that can be applied not only to large graphs but also to dynamically changing networks. We have conducted several tests with different types of evolving networks. We show that our incremental version can process a given large network, faster than the corresponding batch version in both incremental and full dynamic network setups. © Springer International Publishing AG 2018.

Language: English

Type (Professor's evaluation): Scientific