scholarly journals A Hypergraph Data Model for Expert-Finding in Multimedia Social Networks

Information ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 183 ◽  
Author(s):  
Flora Amato ◽  
Giovanni Cozzolino ◽  
Giancarlo Sperlì
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Online Social Networks ◽  
Expert Finding ◽  
Multimedia Social Networks ◽  
Multimedia Recommendation ◽  
New Generation ◽  
Ranking Measures ◽  
Future Work ◽  
Old Friends

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.

scholarly journals Deep Learning-Based Community Detection Approach on Multimedia Social Networks

2021 ◽  
Vol 11 (23) ◽  
pp. 11447
Author(s):  
Antonino Ferraro ◽  
Vincenzo Moscato ◽  
Giancarlo Sperlì
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Data Model ◽  
Multimedia Data ◽  
Expert Finding ◽  
High Data ◽  
Detection Approach ◽  
Multimedia Social Networks ◽  
Different Types ◽  
Community Identification

Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.

Multimedia Social Network Modeling using Hypergraphs

2016 ◽  
Vol 7 (3) ◽  
pp. 53-77 ◽  
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Multimedia Content ◽  
Ranking Functions ◽  
Proposed Model ◽  
Hypergraph Learning ◽  
Efficiency And Effectiveness ◽  
Social Network Modeling ◽  
Multimedia Recommendation ◽  
Combine Information

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

Multimedia Social Networks and E-Learning

2013 ◽  
pp. 365-379
Author(s):  
Andrew Laghos
Keyword(s):  
Social Networks ◽  
Social Interaction ◽  
Learning Communities ◽  
Online Social Networks ◽  
Student Centered ◽  
Student Centered Learning ◽  
Multimedia Social Networks ◽  
Key Players ◽  
E Learning ◽  
Interaction Research

The purpose of this chapter is to investigate Multimedia Social Networks and e-Learning, and the relevant research in these areas. Multimedia Social Networks in e-Learning is an important and evolving study area, since an understanding of the technologies involved as well as an understanding of how the students communicate in online social networks are necessary in order to accurately analyze them. The chapter begins by introducing Multimedia Social Networks and Online Communities. Following this, the key players of e-Learning in Multimedia Social Networks are presented, including a discussion of the different roles that the students take. Furthermore, Social Interaction research is presented concentrating on such important areas as factors that influence social interaction, peer support, student-centered learning, collaboration, and the effect of interaction on learning. The last section of the chapter deals with the various methods and frameworks for analyzing multimedia social networks in e-Learning communities.

Information and Password Attacks on Social Networks

2015 ◽  
Vol 8 (1) ◽  
pp. 25-42 ◽  
Author(s):  
Enrico Franchi ◽  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Real World ◽  
Online Social Networks ◽  
Social Engineering ◽  
Security Measures ◽  
Old Friends ◽  
The Way

Online social networks have changed the way people interact, allowing them to stay in touch with their acquaintances, reconnect with old friends, and establish new relationships with other people based on hobbies, interests, and friendship circles. Unfortunately, the regrettable concurrence of the users' carefree attitude in sharing information, the often sub-par security measures from the part of the system operators and, eventually, the high value of the published information make online social networks an interesting target for crackers and scammers alike. The information contained can be used to trigger attacks to even more sensible targets and the ultimate goal of sociability shared by the users allows sophisticated forms of social engineering inside the system. This work reviews some typical social attacks that are conducted on social networking systems, carrying real-world examples of such violations and analysing in particular the weakness of password mechanisms. It then presents some solutions that could improve the overall security of the systems.

Combining Linked Open Data Similarity and Relatedness for Cross OSN Recommendation

2020 ◽  
Vol 16 (2) ◽  
pp. 59-90
Author(s):  
Mohamed Boubenia ◽  
Abdelkader Belkhir ◽  
Fayçal M'hamed Bouyakoub
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Open Data ◽  
Linked Open Data ◽  
Cross Domain ◽  
Data Similarity ◽  
New Generation

The emergence of online social networks (OSNs) and linked open data (LOD) bring up opportunities to experiment on a new generation of cross-domain recommender systems in which the true benefit of LOD can be exploited, particularly to address the new user problems. In this article, the authors explore the feasibility of combining the two axes of comparison, similarity and relatedness, in LOD space, and introduce a new LOD-based similarity measure. The reason is to take benefit more from LOD to compare general resources, which can be useful in the context of cross-OSN recommendation. Experimental evaluation demonstrates the effectiveness of the proposed approach.

Multimedia Social Network Modeling Using Hypergraphs

2018 ◽  
pp. 636-660
Author(s):  
Giancarlo Sperlì ◽  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Multimedia Content ◽  
Ranking Functions ◽  
Proposed Model ◽  
Hypergraph Learning ◽  
Efficiency And Effectiveness ◽  
Social Network Modeling ◽  
Multimedia Recommendation ◽  
Combine Information

In this paper the authors define a novel data model for Multimedia Social Networks (MSNs), i.e. networks that combine information on users belonging to one or more social communities together with the multimedia content that is generated and used within the related environments. The proposed model relies on the hypergraph data structure to capture and to represent in a simple way all the different kinds of relationships that are typical of social networks and multimedia sharing systems, and in particular between multimedia contents, among users and multimedia content and among users themselves. Different applications (e.g. influence analysis, multimedia recommendation) can be then built on the top of the introduce data model thanks to the introduction of proper user and multimedia ranking functions. In addition, the authors provide a strategy for hypergraph learning from social data. Some preliminary experiments concerning efficiency and effectiveness of the proposed approach for analysis of Last.fm network are reported and discussed.

Information Attacks on Online Social Networks

2014 ◽  
Vol 7 (3) ◽  
pp. 54-71 ◽  
Author(s):  
Enrico Franchi ◽  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Real World ◽  
Online Social Networks ◽  
Social Engineering ◽  
Security Measures ◽  
Old Friends ◽  
The Way

Online social networks have changed the way people interact, allowing them to stay in touch with their acquaintances, reconnect with old friends, and establish new relationships with other people based on hobbies, interests, and friendship circles. Unfortunately, the regrettable concurrence of the users' carefree attitude in sharing information, the often sub-par security measures from the part of the system operators and, eventually, the high value of the published information make online social networks an interesting target for crackers and scammers alike. The information contained can be used to trigger attacks to even more sensible targets and the ultimate goal of sociability shared by the users allows sophisticated forms of social engineering inside the system. This work reviews some typical social attacks that are conducted on social networking systems, carrying real-world examples of such violations and analysing in particular the weakness of password mechanisms. It then presents some solutions that could improve the overall security of the systems.

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