An Hypergraph Data Model for Expert Finding in Multimedia Social Networks

2019 ◽  
pp. 110-120
Author(s):  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Giancarlo Sperlí
Keyword(s):  
Social Networks ◽  
Data Model ◽  
Expert Finding ◽  
Multimedia Social Networks

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.

MapTap and Cornelia Slow Digital Art History and Formal Art Historical Social Network Research

2016 ◽  
Vol 79 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Koenraad Brosens ◽  
Klara Alen ◽  
Astrid Slegten ◽  
Fred Truyen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Early Modern ◽  
Data Model ◽  
Art History ◽  
Digital Art ◽  
Unique Data ◽  
Network Research ◽  
Custom Made

Abstract The essay introduces MapTap, a research project that zooms in on the ever-changing social networks underpinning Flemish tapestry (1620 – 1720). MapTap develops the young and still slightly amorphous field of Formal Art Historical Social Network Research (FAHSNR) and is fueled by Cornelia, a custom-made database. Cornelia’s unique data model allows researchers to organize attribution and relational data from a wide array of sources in such a way that the complex multiplex and multimode networks emerging from the data can be transformed into partial unimode networks that enable proper FAHSNR. A case study revealing the key roles played by women in the tapestry landscape shows how this kind of slow digital art history can further our understanding of early modern creative communities and industries.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117749-117760 ◽  
Author(s):  
Zhiyong Zhang ◽  
Ranran Sun ◽  
Kim-Kwang Raymond Choo ◽  
Kefeng Fan ◽  
Wei Wu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Situation ◽  
Recommendation Algorithm ◽  
Multimedia Social Networks

scholarly journals A Game-Theoretical Approach to Multimedia Social Networks Security

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Enqiang Liu ◽  
Zengliang Liu ◽  
Fei Shao ◽  
Zhiyong Zhang
Keyword(s):  
Social Networks ◽  
Nash Equilibrium ◽  
Access Control ◽  
Control Model ◽  
Security Policies ◽  
Access Control Models ◽  
Multimedia Social Networks ◽  
Game Theoretical Approach ◽  
Game Theoretic ◽  
Stable Combination

The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party’s benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders.

scholarly journals The Transformation Of Marketing By Ai

2021 ◽  
Vol 23 (08) ◽  
pp. 391-410
Author(s):  
K V Bhanu Kiran ◽  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Multimedia Social Networks ◽  
Crucial Part ◽  
Vast Area

From the ages, marketing has been a tough nut to crack as to how thoughtful to deal with the customer’s needs and to reach the specified products to them. Marketing is a vast area to deal with which is a crucial part of any business. In this decade we have a significant innovation to manage such issues effectively, which is Artificial Intelligence. Artificial Intelligence is quite possibly the most brilliant region of science today and can undoubtedly be utilized in the acts of marketing. Platforms for multimedia (social networks, news, images, video, Newsletters, infographics, podcasts, blogs, e-books.) are no longer accessible today are not just for the contact between users or users and companies, but also for companies to guide all aspects of business, collect and identify data of paramount importance. The artificial intelligence marketing technique has become climacteric for companies to find consumer conduct and needs. In this paper, I will walk you through the artificial intelligence marketing technique which transformed marketing into a whole new level. By the end of this paper, you will have a brief idea of how marketing has changed by knowing consumer conduct and needs using artificial intelligence.

Sign in / Sign up

Export Citation Format

Share Document