Network Node Label Acquisition and Tracking

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Sarvenaz Choobdar (Author) Other; Fernando Silva (Author) FCUP; Pedro Ribeiro (Author) FCUP

Conference proceedings International

Title: PROGRESS IN ARTIFICIAL INTELLIGENCE

Pages: 418-430

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

Lisbon, PORTUGAL, OCT 10-13, 2011

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-002-VYN

DOI: 10.1007/978-3-642-24769-9_31

Abstract (EN): Complex networks are ubiquitous in real-world and represent a multitude of natural and artificial systems. Some of these networks are inherently dynamic and their structure changes over time, but only recently has the research community been trying to better characterize them. In this paper we propose a novel general methodology to characterize time evolving networks, analyzing the dynamics of their structure by labeling the nodes and tracking how these labels evolve. Node labeling is formulated as a clustering task that assigns a classification to each node according to its local properties. Association rule mining is then applied to sequences of nodes' labels to extract useful rules that best describe changes in the network. We evaluate our method using two different networks, a real-world network of the world annual trades and a synthetic scale-free network, in order to uncover evolution patterns. The results show that our approach is valid and gives insights into the dynamics of the network. As an example, the derived rules for the scale-free network capture the properties of preferential node attachment.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13