An 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.

A method for chromatin domain partitioning based on hypergraph clustering

“…The quality of a generated hypergraph structure directly affects the effectiveness of data correlation modeling. Hypergraph generation methods can generally be classified into four categories: distance-based methods [29] , representation-based methods [30] , attribute-based methods [31] , and network-based methods [32] .…”