Extending Social Network Diagrams for Large Scale Collaboration

2012 ◽  
pp. 1-13
Author(s):  
I.T. Hawryszkiewycz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Web 2.0 ◽  
Large Scale ◽  
Policy Planning ◽  
Web 2.0 Technologies ◽  
Enterprise Social Networks ◽  
Cloud Technologies ◽  
Common Vision ◽  
Network Diagrams

The chapter provides a way for modeling large scale collaboration using an extension to social network diagrams called enterprise social networks (ESNs). The chapter uses the ESN diagrams to describe activities in policy planning and uses these to define the services to be provided by cloud technologies to support large scale collaboration. This chapter describes collaboration by an architecture made up of communities each with a role to ensure that collaboration is sustainable. The architecture is based on the idea of an ensemble of communities all working to a common vision supported by services provided by the collaboration cloud using Web 2.0 technologies.

The Impact of Web 2.0 Technologies on the Learning Experience of Students in Higher Education

2018 ◽  
Vol 13 (3) ◽  
pp. 1-17 ◽  
Author(s):  
Afaf Mubarak Bugawa ◽  
Andri Mirzal
Keyword(s):  
Higher Education ◽  
Social Networks ◽  
Web 2.0 ◽  
Student Learning ◽  
Learning Experience ◽  
Learning Experiences ◽  
Traditional Classrooms ◽  
Web 2.0 Technologies ◽  
The Impact

This article describes how the use of Web 2.0 technologies in the field of learning is on the rise. By their nature, Web 2.0 technologies increase the interactivity between users where interactivity is considered to be a key to success in traditional classrooms. This article reviews recent studies in the field of Web 2.0 technologies for learning and their impacts on the learning experiences and investigates relationship between Web 2.0 technologies and pedagogy in higher education on student learning. Key findings about the impacts of using social networks like Facebook, Twitter, blogs and wikis on learning experiences are also discussed. Web 2.0 technologies' characteristics and the rationale of Web 2.0 technologies in learning will also be explored.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

scholarly journals Private algorithms for the protected in social network search

2016 ◽  
Vol 113 (4) ◽  
pp. 913-918 ◽  
Author(s):  
Michael Kearns ◽  
Aaron Roth ◽  
Zhiwei Steven Wu ◽  
Grigory Yaroslavtsev
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Data Privacy ◽  
Large Scale ◽  
Graph Search ◽  
Medical Testing ◽  
Background Checks ◽  
Network Search ◽  
Natural Variants

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

Old habits, new dynamics: a case study of Web 2.0 in collaborative humanities and arts research

2011 ◽  
Vol 5 (2) ◽  
pp. 147-157
Author(s):  
Dan Goren
Keyword(s):  
Web 2.0 ◽  
Digital Technology ◽  
Large Scale ◽  
Web 2.0 Technologies ◽  
Research Project ◽  
Research Practices ◽  
Research Environment ◽  
Arts And Humanities ◽  
Virtual Research Environment

Whilst the application of online multimedia digital technology within arts and humanities research has burgeoned over the last decade, the practice of openly conducting collaborative and in particular discursive research publicly online remains one of the most unfamiliar and conceptually problematic areas for many academics in the field. Based on user surveys, blog posts, and forum discussions, this article provides both an account and assessment of Web 2.0 technologies in use on a large-scale arts and humanities research project. Examining usage by and impressions of both the project team and the wider community of users, it investigates both the advantages gained and problems faced through the use of a virtual research environment (VRE). It also pays special attention to the use of video and its implications for research practices.

scholarly journals Measurement of Social Networks for Innovation within Community Disaster Resilience

2019 ◽  
Vol 11 (7) ◽  
pp. 1943 ◽  
Author(s):  
Joanna Wilkin ◽  
Eloise Biggs ◽  
Andrew Tatem
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Field Research ◽  
Disaster Resilience ◽  
Policy Planning ◽  
Critical Methodology ◽  
Research And Policy ◽  
Community Disaster Resilience

Disaster risk reduction (DRR) research has long recognised that social networks are a vital source of support during and after a shock. However, the quantification of this social support, primarily through its recognition as social capital, has proven problematic as there is no singular method for its measurement, invalidating the credibility of studies that try to correlate its effects with community disaster resilience. Within the wider resilience field, research that specifically utilises social networks as the focus of analysis is evolving. This paper provides a critical synthesis of how this developing discourse is filtering into community disaster resilience, reviewing empirical case studies from the Global South within DRR that use social network analysis and connectivity measurement. Our analysis of these studies indicates that a robust methodology utilising social network analysis is emerging, which offers opportunity for research cross-comparability. Our review also finds that without this bottom-up mapping, the implementation of top-down preparedness policy and procedures are likely to fail, resulting in the advocation of social network analysis as a critical methodology in future resilience research and policy planning.

Prediction of Social Influence for Provenance of Misinformation in Online Social Network Using Big Data Approach

2020 ◽  
Author(s):  
Kumaran P ◽  
Rajeswari Sridhar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Law Enforcement ◽  
Online Social Networks ◽  
Large Scale ◽  
Online Social Network ◽  
Root Cause ◽  
Minimum Number ◽  
Targeted Marketing ◽  
Implicit And Explicit

Abstract Online social networks (OSNs) is a platform that plays an essential role in identifying misinformation like false rumors, insults, pranks, hoaxes, spear phishing and computational propaganda in a better way. Detection of misinformation finds its applications in areas such as law enforcement to pinpoint culprits who spread rumors to harm the society, targeted marketing in e-commerce to identify the user who originates dissatisfaction messages about products or services that harm an organizations reputation. The process of identifying and detecting misinformation is very crucial in complex social networks. As misinformation in social network is identified by designing and placing the monitors, computing the minimum number of monitors for detecting misinformation is a very trivial work in the complex social network. The proposed approach determines the top suspected sources of misinformation using a tweet polarity-based ranking system in tandem with sarcasm detection (both implicit and explicit sarcasm) with optimization approaches on large-scale incomplete network. The algorithm subsequently uses this determined feature to place the minimum set of monitors in the network for detecting misinformation. The proposed work focuses on the timely detection of misinformation by limiting the distance between the suspected sources and the monitors. The proposed work also determines the root cause of misinformation (provenance) by using a combination of network-based and content-based approaches. The proposed work is compared with the state-of-art work and has observed that the proposed algorithm produces better results than existing methods.

scholarly journals Social Media And Networking Technologies: An Analysis Of Collaborative Work And Team Communication

2012 ◽  
Vol 5 (4) ◽  
pp. 295 ◽  
Author(s):  
Ephraim A. Okoro ◽  
Angela Hausman ◽  
Melvin C. Washington
Keyword(s):  
Social Networks ◽  
Web 2.0 ◽  
Learning Outcomes ◽  
Group Work ◽  
Collaborative Work ◽  
Faculty Members ◽  
Active Engagement ◽  
Web 2.0 Technologies ◽  
Quality Instruction ◽  
Networking Technologies

Digital communication increases students learning outcomes in higher education. Web 2.0 technologies encourages students active engagement, collaboration, and participation in class activities, facilitates group work, and encourages information sharing among students. Familiarity with organizational use and sharing in social networks aids students who are expected to be facile in these technologies upon graduation (Benson, Filippaios, and Morgan, 2010). Faculty members become coaches, monitoring and providing feedback to students rather than directing activities. While Web 2.0 technologies, including social networks, may act as a distraction in a teaching environment, our findings suggest that effective social networking in learning environments sustain quality instruction and skills-development in business education.

scholarly journals Digital Diplomacy in Russian-European Relations: Cross-Cultural Aspects

2021 ◽  
Vol 5 (4) ◽  
pp. 30-40
Author(s):  
E. S. Zinovieva ◽  
V. I. Bulva
Keyword(s):  
Social Networks ◽  
Foreign Policy ◽  
Web 2.0 ◽  
Intercultural Communication ◽  
Information And Communication Technologies ◽  
International Organizations ◽  
Web 2.0 Technologies ◽  
Eu Countries ◽  
Digital Diplomacy ◽  
The Eu

The development of information and communication technologies and formation of the global information society actualizes the study of new directions in the evolution of diplomatic practice in the digital environment, including in the context of intercultural communication. The modern information revolution is characterized by the widespread and ever-growing use of social networks, blogs, wiki resources and other media platforms (labelled under the common term of Web 2.0 technologies). At the same time, the widespread use of Web 2.0 technologies and the increasing amount of time people all over the world spend there has a wide and profound impact on political and intercultural communication and diplomatic practice. A new phenomenon of digital diplomacy is gaining prominence among foreign policy tools of states and international organizations. Digital diplomacy can be defined as the use of social networks and Web 2.0 technologies in public diplomacy and international interaction by states and international organizations to achieve foreign policy goals and reach foreign audiences. According to the traditional view of digital diplomacy, which has developed in the academic literature, and is reflected in the works of authors such as M. Castells and J. Nye, it helps to strengthen network ties at the level of civil societies in different countries and thus reduces international conflicts. However, cultural differences and digital polarization can impede the potential of digital diplomacy. Today, almost all states and international organizations in the global arena are involved in the practice of digital diplomacy, and Russia is no exception. Russia actively participates in the digital diplomacy practice, by using social media and Web 2.0 tools as soft power instruments to introduce and explain foreign policy initiatives and reach foreign and domestic audiences, as stated in the Doctrine of the Information Security of Russian Federation of 2016. For Russia's foreign policy, relations with the EU countries and EU institutions are of particular importance, including in the digital sphere. However, even though both Russia and the EU countries make extensive use of digital diplomacy tools, the practice of horizontal network interaction mediated by digital technologies does not contribute to strengthening trust between countries and reducing conflicts. The authors consider incidents and allegations in the sphere of digital interaction and, based on the theory of digital polarization, conclude that the use of digital tools in horizontal interactions within digital diplomacy exacerbates intercultural differences between countries and increases conflict instead of improving mutual understanding.

Sign in / Sign up

Export Citation Format

Share Document