Dynamic graph summarization: a tensor decomposition approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2018

Autores

Fernandes, S (Autor) Outra; Fanaee T, H (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Data Mining and Knowledge Discovery

Vol. 32

Páginas: 1397-1420

ISSN: 1384-5810

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 10 Citações

Scopus - 11 Citações

Outras Informações

ID Authenticus: P-00P-31F

DOI: 10.1007/s10618-018-0583-9

Abstract (EN): Due to the scale and complexity of todays' social networks, it becomes infeasible to mine them with traditional approaches. A possible solution to reduce such scale and complexity is to produce a compact (lossy) version of the network that represents its major properties. This task is known as graph summarization, which is the subject of this research. Our focus is on time-evolving graphs, a more complex scenario where the dynamics of the network also should be taken into account. We address this problem using tensor decomposition, which enables us to capture the multi-way structure of the time-evolving network. This property is unique and is impossible to obtain with other approaches such as matrix factorization. Experimental evaluation on five real world networks implies promising results demonstrating that tensor decomposition is quite useful for summarizing dynamic networks.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 24