Evolutionary role mining in complex networks by ensemble clustering

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

Authors

Choobdar, S (Author) Other; Pedro Ribeiro (Author) FCUP; Silva, F (Author) FCUP

Conference proceedings International

Title: Proceedings of the Symposium on Applied Computing, SAC 2017, Marrakech, Morocco, April 3-7, 2017

Pages: 1053-1060

Indexing

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00M-TP3

DOI: 10.1145/3019612.3019815

Abstract (EN): The structural patterns in the neighborhood of nodes assign unique roles to the nodes. Mining the set of existing roles in a network provides a descriptive profile of the network and draws its general picture. This paper proposes a new method to determine structural roles in a dynamic network based on the current position of nodes and their historic behavior. We develop a temporal ensemble clustering technique to dynamically find groups of nodes, holding similar tempo-structural roles. We compare two weighting functions, based on age and distribution of data, to incorporate temporal behavior of nodes in the role discovery. To evaluate the performance of the proposed method, we assess the results from two points of view: 1) goodness of fit to current structure of the network; 2) consistency with historic data. We conduct the evaluation using different ensemble clustering techniques. The results on real world networks demonstrate that our method can detect tempo-structural roles that simultaneously depict the topology of a network and reflect its dynamics with high accuracy. Copyright 2017 ACM.

Language: English

Type (Professor's evaluation): Scientific