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.

Tag's Depth-Based Expert Profiling Using a Topic Modeling Technique

2020 ◽  
Vol 16 (4) ◽  
pp. 81-99
Author(s):  
Saida Kichou ◽  
Omar Boussaid ◽  
Abdelkrim Meziane
Keyword(s):  
Social Networks ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
The Other ◽  
Modeling Technique ◽  
Social Tagging ◽  
Expert Finding ◽  
Stack Overflow ◽  
Allocation Algorithm ◽  
The Right

Expert finding and expert profiling are two important tasks for organizations, researchers, and work seekers. This importance can also be seen in online communities especially with the explosion of social networks. Expert finding on one hand addresses the task of finding the right person with the appropriate knowledge or skills. Expert profiling on the other hand gives a concise and meaningful description of a candidate expert. This paper focuses on what social tagging can bring to improve expert finding and profiling. A novel expertise indicator that models and assesses an expert based on the expert's tagging activities is proposed. First, tags are used as interest indicator to build candidate's profiles; then, Latent Dirichlet Allocation algorithm (LDA) is used to construct the tags distribution over topics by exploiting the tag's semantic characteristics. Topics of interest are then filtered using tag's depth. The latter is finally used as the expertise indicator. Experiments performed on the stack overflow dataset show the accuracy of the proposed approach.

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.

scholarly journals Expert Finding Considering Dynamic Profiles and Trust in Social Networks

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1165
Author(s):  
Kyoungsoo Bok ◽  
Inbae Jeon ◽  
Jongtae Lim ◽  
Jaesoo Yoo
Keyword(s):  
Social Networks ◽  
Performance Evaluation ◽  
Social Network ◽  
Network Services ◽  
Expert Finding ◽  
Recent Activity ◽  
Social Network Services ◽  
Professional Information ◽  
Dynamic Profile ◽  
User Trust

Recently, social network services that express individual opinions and thoughts have been significantly developed. As unreliable information is generated and shared by arbitrary users in social network services, many studies have been conducted to find users who provide reliable and professional information. In this paper, we propose an expert finding scheme to discover users who can answer users’ questions professionally in social network services. We use a dynamic profile to extract the user’s latest interest through an analysis of the user’s recent activity. To improve the accuracy of the expert finding results, we consider the user trust and response quality. We conduct a performance evaluation with the existing schemes through various experiments to verify the superiority of the proposed scheme.

Misinformation-oriented expert finding in social networks

2019 ◽  
Vol 23 (2) ◽  
pp. 693-714 ◽  
Author(s):  
Guohui Li ◽  
Ming Dong ◽  
Fuming Yang ◽  
Jun Zeng ◽  
Jiansen Yuan ◽  
...  
Keyword(s):  
Social Networks ◽  
Expert Finding

Multilayer Social Networks

2016 ◽  
Author(s):  
Mark E. Dickison ◽  
Matteo Magnani ◽  
Luca Rossi
Keyword(s):  
Social Networks ◽  
Multilayer Social Networks
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