Social networks are becoming increasingly important in many fields, from marketing analysis to bioinformatics. Link prediction processes are essential tasks required for analysis of the networks’ structures. In this paper, we propose a fuzzy computational model, called Fuzzy Social Signature, to represent a network from the perspective of a single user. This model assumes that not all links are equally important and that the relationships between nodes of a social network can be vague and uncertain. Based on the proposed Fuzzy Social Signature, a preliminary technique for link prediction between users performing same activities is proposed. Encouraging results have been obtained with an initial set of experiments using a real-world dataset.
Link Prediction in Signed Social Networks using Fuzzy Signature
Giuseppe D'Aniello;Matteo Gaeta;
2019-01-01
Abstract
Social networks are becoming increasingly important in many fields, from marketing analysis to bioinformatics. Link prediction processes are essential tasks required for analysis of the networks’ structures. In this paper, we propose a fuzzy computational model, called Fuzzy Social Signature, to represent a network from the perspective of a single user. This model assumes that not all links are equally important and that the relationships between nodes of a social network can be vague and uncertain. Based on the proposed Fuzzy Social Signature, a preliminary technique for link prediction between users performing same activities is proposed. Encouraging results have been obtained with an initial set of experiments using a real-world dataset.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
