Paper Info
Title
A Hypergraph Data Model For Expert-Finding In Multimedia Social Networks
Abstract
Online Social Networks (OSNs) have found widespread applications in every area of our life. A large number of people have signed up to OSN for different purposes, including to meet old friends, to choose a given company, to identify expert users about a given topic, producing a large number of social connections. These aspects have led to the birth of a new generation of OSNs, called Multimedia Social Networks (MSNs), in which user-generated content plays a key role to enable interactions among users. In this work, we propose a novel expert-finding technique exploiting a hypergraph-based data model for MSNs. In particular, some user-ranking measures, obtained considering only particular useful hyperpaths, have been profitably used to evaluate the related expertness degree with respect to a given social topic. Several experiments on Last.FM have been performed to evaluate the proposed approach's effectiveness, encouraging future work in this direction for supporting several applications such as multimedia recommendation, influence analysis, and so on.
Year
DOI
Venue
2019
10.3390/info10060183
INFORMATION
Keywords
Field
DocType
multimedia social networks, social network analysis, expert-finding, hypergraphs
Data science,Social network,Computer science,Hypergraph,Constraint graph,Social network analysis,Influence analysis,Multimedia social networks,Artificial intelligence,Data model,Machine learning
Journal
Volume
Issue
ISSN
10
6
2078-2489
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Flora Amato145866.48
Giovanni Cozzolino2315.60
Giancarlo Sperli38619.40