Network Node Label Acquisition and Tracking

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Sarvenaz Choobdar (Autor) Outra; Fernando Silva (Autor) FCUP; Pedro Ribeiro (Autor) FCUP

Ata de Conferência Internacional

Título: PROGRESS IN ARTIFICIAL INTELLIGENCE

Páginas: 418-430

15th Portuguese Conference on Artificial Intelligence (EPIA 2011)

Lisbon, PORTUGAL, OCT 10-13, 2011

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13