Personalization of the collaborator recommendation system in multi‐layer scientific social networks: A case study of ResearchGate

2021 ◽  
Author(s):  
Zahra Roozbahani ◽  
Jalal Rezaeenour ◽  
Ali Katanforoush ◽  
Amir Jalaly Bidgoly
Keyword(s):  
Social Networks ◽  
Recommendation System

Mobility and Immobility in a Transnational Context: Changing Views of Migration among the Kazakh Diaspora in Mongolia

2006 ◽  
Vol 3 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Cynthia Werner ◽  
Holly R. Barcus
Keyword(s):  
Social Networks ◽  
Transnational Migration ◽  
Access To Information ◽  
Kazakh Population ◽  
Methodological Approaches

Inquiry into the causes and outcomes of transnational migration spans numerous disciplines, scales and methodological approaches.  Fewer studies focus on immobility.  Utilizing the Kazakh population of Mongolia as a case study, this paper considers how non-migrants view the economic and cultural costs of migrating.  We posit that three factors, including local place attachments specific to Mongolia, access to information about life in Kazakhstan and the importance of maintaining social networks in Mongolia, contribute substantially to their decision to not migrate. Our findings suggest that the decision to not migrate can be very strategic for non-migrants in highly transnational contexts.  

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

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 Debate on online social networks at the time of COVID-19: An Italian case study

2021 ◽  
Vol 23 ◽  
pp. 100136
Author(s):  
Martino Trevisan ◽  
Luca Vassio ◽  
Danilo Giordano
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Italian Case

scholarly journals “Splendid Isolation”: The reproduction of music industry inequalities in Spotify’s recommendation system

2021 ◽  
pp. 146144482110221
Author(s):  
Tamas Tofalvy ◽  
Júlia Koltai
Keyword(s):  
Network Analysis ◽  
Recommendation System ◽  
Music Industry ◽  
Country Of Origin ◽  
Small Scale ◽  
Primary Determinant ◽  
Scale Network ◽  
Splendid Isolation

In this article, we argue that offline inequalities, such as core–periphery relations of the music industry, are reproduced by streaming platforms. First, we offer an overview of the reproduction of inequalities and core–periphery dynamics in the music industry. Then we illustrate this through a small-scale network analysis case study of Hungarian metal bands’ connections on Spotify. We show that the primary determinant of a given band’s international connectedness in Spotify’s algorithmic ecosystem is their international label connections. Bands on international labels have more reciprocal international connections and are more likely to be recommended based on actual genre similarity. However, bands signed with local labels or self-published tend to have domestic connections and to be paired with other artists by Spotify’s recommendation system according to their country of origin.

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

Conceptualizing International Labor Migration: A Structuration Perspective

1995 ◽  
Vol 29 (2) ◽  
pp. 317-351 ◽  
Author(s):  
Jon Goss ◽  
Bruce Lindquist
Keyword(s):  
Social Networks ◽  
Labor Migration ◽  
Structuration Theory ◽  
The Philippines ◽  
Levels Of Analysis ◽  
International Labor ◽  
Anthony Giddens ◽  
Theory Of Structuration ◽  
International Labor Migration

This article applies the theory of structuration to international labor migration using case study material from the Philippines. It first provides a brief review of the functional and structural approaches to understanding labor migration and the theoretical impasse that has been created between them. It then reviews several attempts to resolve this impasse, including systems and networks approaches; these solutions are rejected on theoretical and empirical grounds. We suggest that migrant institutions may be a more appropriate mid-level concept than households or social networks to articulate various levels of analysis. We develop this concept in the context of the structuration theory of Anthony Giddens and attempt to apply this to the Philippines, concluding that this framework is eminently suited for further research on international labor migration.

scholarly journals Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 23
Author(s):  
Angeliki Kitsiou ◽  
Eleni Tzortzaki ◽  
Christos Kalloniatis ◽  
Stefanos Gritzalis
Keyword(s):  
Social Networks ◽  
Interdisciplinary Approach ◽  
Privacy Preserving ◽  
Social Characteristics ◽  
Behavioral Models ◽  
Social Landscape ◽  
Technological Context ◽  
Privacy Risks ◽  
Self Adaptive

Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

2021 ◽  
Author(s):  
Yang Hu ◽  
Yiwen Ding ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Industrial Robots ◽  
Knowledge Graph ◽  
Disassembly Process ◽  
Knowledge Based ◽  
Manufacturing Efficiency ◽  
Human Operators ◽  
Traditional Manufacturing ◽  
Human Robot Collaboration

Abstract In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

Sign in / Sign up

Export Citation Format

Share Document