The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...Sorin Adam Matei
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
2009 Node XL Overview: Social Network Analysis in Excel 2007Marc Smith
A quick overview of the features of NodeXL, the network overview, discovery, and exploration add-in for Excel 2007. This tool allows for visualizing directed graphs and social networks within Excel. It provides several network metrics and manipulation tools. Networks can be imported from Twitter and personal email.
Visible Effort: A Social Entropy Methodology for Managing Computer-Mediated ...Sorin Adam Matei
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
2009 Node XL Overview: Social Network Analysis in Excel 2007Marc Smith
A quick overview of the features of NodeXL, the network overview, discovery, and exploration add-in for Excel 2007. This tool allows for visualizing directed graphs and social networks within Excel. It provides several network metrics and manipulation tools. Networks can be imported from Twitter and personal email.
Methodology .. Visualization .. Note : the content of this presentation isn't mine .. I searched on net and summarized lots of points in this topic then made this PP .. I hope it will be useful for you ..
Vipassana Meditation: why you should spend ten days in silencelovekaran567
I don’t practice Vipassana meditation, leaning towards more concentration-based techniques instead, but I think vipassana is an excellent meditation technique for beginners and everyone should do a ten-day silent Vipassana meditation retreat.
Stop Thinking - How to Reduce Your Thinking to Help Create Quality IdeasPaul Foreman
Stop Thinking - How to Reduce Your Thinking to Help Create Quality Ideas
You can subscribe to the Mind Map Inspiration Blog to receive new Mind Maps at http://www.mindmapinspiration.com/ and follow me on Twitter @mindmapdrawer http://twitter.com/mindmapdrawer
Also available: E-Books designed to help you create stylish and artistic mind maps of your own - visit the Mind Map Inspiration Website for more details: http://www.mindmapinspiration.co.uk/
Your body is communicating with you all the time. Are you listening? See what changes for you when you start paying attention - and use the wisdom of the body to assist you in making choices!
This file presents a table of 30 visualization tools that we reviewed on the basis of kind of feedback that they are able to provide. Moreover, I afford you also a brief description of tools.
TOPIC networking portfolio
ACADEMIC LEVEL Undergrad. (yrs 3-4)
DISCIPLINE Business Studies
DOCUMENT TYPE Term paper
SPACING DOUBLE
CITATION STYLE Harvard
Icwsm10 S MateiVisible Effort: A Social Entropy Methodology for Managing Com...guest803e6d
A theoretically-grounded learning feedback tool suite, the Visible Effort (VE) Mediawiki extension, is proposed for optimizing online group learning activities by measuring the amount of equality and the emergence of social structure in groups that participate in Computer-Mediated Collaboration (CMC). Building on social entropy theory, drawn from Shannon’s Mathematical Theory of Communication, VE captures levels of CMC unevenness and group structure and visualizes them on wiki Web pages through background colors, charts, and tabular data. Visual information provides users entropic feedback on how balanced and equitable collaboration is within their online group are, while helping them to maintain it within optimal levels. Finally, we present the theoretical and practical implications of VE and the measures behind it, as well as illustrate VE’s capabilities by describing a quasi-experimental teaching activity (use scenario) in tandem with a detailed discussion of theoretical justification, methodological underpinning, and technological capabilities of the approach.
The Mathematics of Social Network Analysis: Metrics for Academic Social NetworksEditor IJCATR
Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a
social network. Such networks can be casual, like those on social media sites, or formal, like academic social networks. Each of these
networks is characterised by underlying data which defines various features of the network. Keeping in view the size and diversity of
these networks it may not be possible to dissect entire network with conventional means. Social network visualization can be used to
graphically represent these networks in a concise and easy to understand manner. Social network visualization tools rely heavily on
quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics
used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that
are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also
outlined
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
2009-JCMC-Discussion catalysts-Himelboim and SmithMarc Smith
This study addresses 3 research questions in the context of online political discussions:
What is the distribution of successful topic starting practices, what characterizes the content
of large thread-starting messages, and what is the source of that content? A 6-month
analysis of almost 40,000 authors in 20 political Usenet newsgroups identified authors
who received a disproportionate number of replies. We labeled these authors ‘‘discussion
catalysts.’’ Content analysis revealed that 95 percent of discussion catalysts’ messages
contained content imported from elsewhere on the web, about 2/3 from traditional news
organizations. We conclude that the flow of information from the content creators to the
readers and writers continues to be mediated by a few individuals who act as filters and
amplifiers.