Name: Matrix Factorization Framework for Overlapping Community Detection: Modeling the Relationship between Links and Communities (Paperback) (en Inglés)
Price: 198.59 PEN
Availability: InStock
Author: Hongyi Zhang Irwin King Micheal R. Lyu
ISBN: 9786139894772

portada Matrix Factorization Framework for Overlapping Community Detection: Modeling the Relationship between Links and Communities (Paperback) (en Inglés)

Matrix Factorization Framework for Overlapping Community Detection: Modeling the Relationship between Links and Communities (Paperback) (en Inglés)

Formato

Libro Físico

Editorial

LAP Lambert Academic Publishing, Germany

Autor

Hongyi Zhang Irwin King Micheal R. Lyu

Idioma

Inglés

N° páginas

112

Encuadernación

Tapa Blanda

ISBN13

9786139894772

Matrix Factorization Framework for Overlapping Community Detection: Modeling the Relationship between Links and Communities (Paperback) (en Inglés)

Hongyi Zhang Irwin King Micheal R. Lyu (Autor) · LAP Lambert Academic Publishing, Germany · Tapa Blanda

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Reseña del libro "Matrix Factorization Framework for Overlapping Community Detection: Modeling the Relationship between Links and Communities (Paperback) (en Inglés)"

Community detection is an important line of research in understanding complex networks. In many real-world networks, communities naturally overlap since a node usually has multiple community memberships, which makes overlapping community detection a trend in recent years. One popular technique to cope with overlapping community detection is matrix factorization (MF). In this book, we review existing models in matrix factorization framework for overlapping community detection and propose four novel models to explore the relationship between links and communities. By evaluate our proposed models on several real-world networks including large ones with ground-truth communities, we show that our models outperform state-of-the-art approaches on multiple measurements and are capable of finding overlapping communities with better quality. This book should be especially useful to researchers focusing on community detection, or anyone else who is interested in social networks.