Search results “Sna social network analysis”
What is Social Network Analysis?
You use social networks every day, but how can we understand how they work to affect our decisions, our careers, our health, and our histories? The field of Social Network Analysis is the dynamic and highly adaptable group of techniques that let us quantify and understand the complex structures and flows of relationships, thoughts, and things between people around the world. Look at your own social networks at these links: Check your own personal Facebook social network with Touchgraph: http://www.touchgraph.com/facebook Check your own personal LinkedIn social network with Socilab: http://socilab.com/ Check your own personal Twitter social network with Mentionmapp: http://mentionmapp.com/ Social Network Analysis can enrich the research of faculty and the studies of students—look for workshops run by the Duke Network Analysis Center and classes featuring graph theory, network theory, and social networks. Networks are everywhere—what will you discover with them?
Basics of Social Network Analysis
Basics of Social Network Analysis In this video Dr Nigel Williams explores the basics of Social Network Analysis (SNA): Why and how SNA can be used in Events Management Research. The freeware sound tune 'MFF - Intro - 160bpm' by Kenny Phoenix http://www.last.fm/music/Kenny+Phoenix was downloaded from Flash Kit http://www.flashkit.com/loops/Techno-Dance/Techno/MFF_-_In-Kenny_Ph-10412/index.php The video's content includes: Why Social Network Analysis (SNA)? Enables us to segment data based on user behavior. Understand natural groups that have formed: a. topics b. personal characteristics Understand who are the important people in these groups. Analysing Social Networks: Data Collection Methods: a. Surveys b. Interviews c. Observations Analysis: a. Computational analysis of matrices Relationships: A. is connected to B. SNA Introduction: [from] A. Directed Graph [to] B. e.g. Twitter replies and mentions A. Undirected Graph B. e.g. family relationships What is Social Network Analysis? Research technique that analyses the Social structure that emerges from the combination of relationships among members of a given population (Hampton & Wellman (1999); Paolillo (2001); Wellman (2001)). Social Network Analysis Basics: Node and Edge Node: “actor” or people on which relationships act Edge: relationship connecting nodes; can be directional Social Network Analysis Basics: Cohesive Sub-group Cohesive Sub-group: a. well-connected group, clique, or cluster, e.g. A, B, D, and E Social Network Analysis Basics: Key Metrics Centrality (group or individual measure): a. Number of direct connections that individuals have with others in the group (usually look at incoming connections only). b. Measure at the individual node or group level. Cohesion (group measure): a. Ease with which a network can connect. b. Aggregate measure of shortest path between each node pair at network level reflects average distance. Density (group measure): a. Robustness of the network. b. Number of connections that exist in the group out of 100% possible. Betweenness (individual measure): a. Shortest paths between each node pair that a node is on. b. Measure at the individual node level. Social Network Analysis Basics: Node Roles: Node Roles: Peripheral – below average centrality, e.g. C. Central connector – above average centrality, e.g. D. Broker – above average betweenness, e.g. E. References and Reading Hampton, K. N., and Wellman, B. (1999). Netville Online and Offline Observing and Surveying a Wired Suburb. American Behavioral Scientist, 43(3), pp. 475-492. Smith, M. A. (2014, May). Identifying and shifting social media network patterns with NodeXL. In Collaboration Technologies and Systems (CTS), 2014 International Conference on IEEE, pp. 3-8. Smith, M., Rainie, L., Shneiderman, B., and Himelboim, I. (2014). Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. Pew Research Internet Project.
Views: 41959 Alexandra Ott
Big Data - Apa itu Social Network Analysis (SNA)?
Penerapan salah satu metode Big Data yaitu Social Network Analysis dengan mengambil contoh satu kata kunci (keyword) untuk di analisis
Views: 386 Bima Oktavio Putra
Social Network Analysis with Lada Adamic
The course "Social Network Analysis", by Associate Professor Lada Adamic from the University of Michigan, will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/sna
Views: 20875 CourseraVideos
Social Network Analysis
Brief info on how to conduct Social Network Analysis (SNA)
Views: 5932 KMPlus Consulting
Social network analysis - Introduction to structural thinking: Dr Bernie Hogan, University of Oxford
Social networks are a means to understand social structures. This has become increasingly relevant with the shift towards mediated interaction. Now we can observe and often analyse links at a scale that far outpaces what was possible only decades ago. While this prompts new methodologies, the large-scale networks we can observe can still be informed by classis questions in social network analysis. In this class, we take a brisk tour through the classic ideas of social network analysis including preferential attachment, small worlds, homophily, the friendship paradox and clustering. Bernie demonstrates how these ideas are not only applicable to modern digital networks but have been updated with interesting insights fromdata on Twitter, Facebook and the World Wide Web itself. This is an introductory class, an advanced class session is planned for 2018. Readings: Hidalgo, C.A. (2016). Disconnected, fragmented, or united? A trans-disciplinary review of network science. Applied Network Science, 1(6), 1-19 . http://doi.org/10.1007/s41109-016-0010-3 Hogan, B. (2017). Online Social Networks: Concepts for Data Collection and Analysis. In Fielding, N.G., Lee, R., & Blank, G. (eds). The Sage Handbook of Online Research Methods. Thousand Oaks, Ca: Sage Publications. Pp. 241-258 Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3047869 Harrington, H.A., Beguerisse-diaz, M., Rombach, M.P., Keating, L. M., & Porter, M.A. (2013). Commentary: Teach network science to teenagers. Network Science, 1(2), 226-247. http://doi.org/10.1017/nws.2013.11 #datascienceclasses
Network Analysis. Lecture10. Community detection
Community detection algorithms. Overlapping communities. Clique percolation method. Heuristic methods. Label propagation. Fast community unfolding. Random walk based methods. Walktrap. Nibble. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture10.pdf
Views: 11941 Leonid Zhukov
Gephi Tutorial - How to use Gephi for Network Analysis
Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Gephi is an open-source network analysis software package written in Java that allows us to visualize all kinds of graphs and networks. In this Gephi tutorial, we walk through how Network Analysis can be used to visually represent large data sets in a way that enables the viewer to get a lot of value from the data just by looking briefly at the graph. Watch this video to learn: - What Network Analysis involves - How to use Gephi to visually represent and analyze data sets - Different examples using Gephi
Views: 25597 Fullstack Academy
Introduction to SNA.  Lecture 2. Descriptive Network Analysis
Lecture slides: https://drive.google.com/file/d/0B7-pBlaW03HaMTk5QVNwVmZrbGc/view?usp=sharing Basic graph theory. Node degree distribution. Graph diameter and average path length. Clustering coefficient. Real world examples
Views: 1746 Leonid Zhukov
Introduction to Social Network Analysis
This workshop provides a broad overview of Social Network Analysis. In the first part of the workshop, a concise overview of theoretical concepts is provided, together with examples of data collection methods. The second section discusses network data analysis - network measurements (i.e. density, reciprocity, etc.) and node level measurements (i.e. degree centrality, betweenness centrality, etc.). The last part of the workshop introduces participants to UCINET and NetDraw, software packages used for data management, analysis and visualization.
SAS Social Network Analysis. Part1
Знакомство с продуктом SAS Social Network Analysis 6.2
Introduction to SNA. Lecture 5. Network communities.
Cohesive subgroups. Graph cliques. Network communities. Graph partitioning. Modularity. Edge Betweenness. Spectral partitioning. Modularity maximization. Heuristic methods. Label propagation. Fast community unfolding. Walktrap. Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture5.pdf
Views: 2952 Leonid Zhukov
Importing Social Network Data into R through CSV Files
This video walks through the process of loading social network data into R for use with the package igraph by 1) typing in a short edge list into an R script), 2) importing a CSV file of an edge list, 3) importing a CSV file of an adjacency matrix. Shot for the University of Maine at Augusta
Views: 14045 James Cook
Network Centrality
Follow along with the course eBook: http://bit.ly/2JymqYp See the full course: https://systemsacademy.io/courses/network-theory/ Twitter: http://bit.ly/2HobMld In this module, we talk about one of the key concepts in network theory, centrality. Centrality gives us some idea of the node's position within the overall network and it is also a measure that tells us how influential or significant a node is within a network although this concept of significance will have different meanings depending on the context. Transcription: In the previous module we talked about the degree of connectivity of a given node in a network and this leads us to the broader concept of centrality. Centrality is really a measure that tells us how influential or significant a node is within the overall network, this concept of significance will have different meanings depending on the type of network we are analyzing, so in some ways centrality indices are answers to the question "What characterizes an important node?" From this measurement of centrality we can get some idea of the nodes position within the overall network. The degree of a node’s connectivity that we previously looked at is probably the simples and most basic measure of centrality. We can measure the degree of a node by looking at the number of other nodes it is connected to vs. the total it could possibly be connected to. But this measurement of degree only really captures what is happening locally around that node it don’t really tell us where the node lies in the network, which is needed to get a proper understanding of its degree centrality and influence. This concept of centrality is quite a bit more complex than that of degree and may often depend on the context, but we will present some of the most important parameters for trying to capture the significance of any given node within a network. The significance of a node can be thought of in two ways, firstly how much of the networks recourses flow through the node and secondly how critical is the node to that flow, as in can it be replaced, so a bridge within a nations transpiration network may be very significant because it carries a very large percentage of the traffic or because it is the only bridge between two important locations. So this helps us understand significance on a conceptual level but we now need to define some concrete parameters to capture and quantify this intuition. We will present four of the most significant metric for doing this here; Firstly as we have already discussed a nodes degree of connectivity is a primary metric that defined its degree of significance within its local environment. Secondly, we have what are called closeness centrality measures that try to capture how close a node is to any other node in the network that is how quickly or easily can the node reach other nodes. Betweenness is a third metric we might use, which is trying to capture the nodes role as a connector or bridge between other groups of nodes. Lastly we have prestige measures that are trying to describe how significant you are based upon how significant the nodes you are connect to are. Again which one of these works best will be context dependent. So to talk about closeness then; closeness maybe defined as the reciprocal of farness where the farness of a given node is defined as the sum of its distances to all other nodes. Thus, the more central a node is the lower its total distance to all other nodes. Closeness can be regarded as a measure of how long it will take to spread something such as information from the node of interest to all other nodes sequentially; we can understand how this correlates to the node’s significance in that it is a measurement of the nodes capacity to effect all the other elements in the network. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 31727 Systems Academy
Network Analysis. Lecture 5. Centrality measures.
Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Katz status index and Bonacich centrality, alpha centrality. Spearman rho and Kendall-Tau ranking distance. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture5.pdf
Views: 18993 Leonid Zhukov
Example of basic Social Network Analysis of Facebook friends
http://paddytherabbit.com/example-facebook-friends-analysis/ I am using the Louvain method method for community detection
Views: 6312 David Sherlock
Analysis - Social Network Analysis SNA - Analyst's Notebook - A Tech Support Guide
This video is part of a series on IBM i2’s Tech Support Guide to Analyst’s Notebook. The playlist can be found here https://www.youtube.com/playlist?list=PLFip581NcL2Uxv_6cIRvZXSCo7JjMovqU and the first video as part of the playlist can be found here https://www.youtube.com/watch?v=YqVqcYgPtvw&list=PLFip581NcL2Uxv_6cIRvZXSCo7JjMovqU&index=1 Legal details regarding this video can be found at the following link. http://ibm.biz/SecVidLegalDisclaimer
Social Network Analysis with R | Examples
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 26227 Dr. Bharatendra Rai
The Basics of Social Network Analysis: A Social Network Lab in R for Beginners
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So you want to get started with social network analysis but need a foundation or a refresher? This video covers exactly what we mean by a “network” and is the start of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Mini Lecture: Social Network Analysis for Fraud Detection
In this mini lecture, Véronique Van Vlasselaer talks about how social networks can be leveraged to uncover fraud. Véronique is working in the DataMiningApps group led by Prof. dr. Bart Baesens at the KU Leuven (University of Leuven), Belgium.
Views: 15118 Bart Baesens
An Introduction to Social Network Analysis |   BLG Data Research Centre
In this webinar, Dr Marianna Marra will introduce participants to social network analysis. Social network analysis (SNA) is the process of investigating and measuring social relationships between people, organisations and technology. SNA is very useful in mapping different kind of relationships and detect data patterns in economic and knowledge networks. Dr Marianna Marra is lecturer in Management Science at Essex Business School. She completed her PhD in Management Science at Aston Business School. Her research interests are: Social Network Analysis (SNA); Innovation; Knowledge transfer and acquisition processes; Patent data analysis. This webinar aims to provide attendees with an introductory understanding of networks’ dynamics. No prior knowledge is required. Exploring Data | Enhancing Knowledge | Empowering Society Follow us to find out more: Twitter @BLGDataResearch http://www.blgdataresearch.org/
Social network analysis for journalists using the Twitter API
In an age of social media, social network analysis (SNA) is becoming a promising technique for the digital journalist's toolkit. SNA allows journalists to uncover relationships between individuals and organisations, and identify key players and relevant peer groups, by using information on how people and organisations are connected to each other. In this workshop we will use Twitter data from around the journalism festival and analyse it to reveal connections between festival participants. Participants will: collect data from the Twitter API based on a specific hashtag or keyword; identify and record interactions within the dataset; analyse and visualize the dataset using Gephi. Based on this simple exercise, participants will be able to perform their own Twitter data collection as well as social network analysis on similar datasets. Please download the Gephi visualisation software from gephi.org and install it on your computer in advance. Participants are required to have basic social media and spreadsheet skills. This session is part of the School of Data Journalism organised in association with European Journalism Centre and Open Knowledge Foundation Michael Bauer Open Knowledge Foundation
What is Social Network Analysis (SNA)?
What is Social Network Analysis (SNA)? Why is it so useful for understanding social connections?
Views: 6 Dean Lusher
Integration of Social Network Analysis (SNA) and Spatial Analysis (GIS)
This webinar will discuss two Social Network Analysis projects that the Philadelphia Police Department undertook. The first project examined the extent of shared connections among shooting victims through network analysis; in particular, the analysis examined cross-divisional connections by combining the network analysis and GIS. The second project applied SNA to understand connections among gangs at the group level across the city. The project focused on 1) identifying the extent and nature of positive/negative connections among gangs and 2) developing a web-based application that visualizes the result of SNA on a map. Presenters: George Kikuchi, Research & Information Analyst Supervisor, Philadelphia Police George is a supervisory analyst at the Delaware Valley Intelligence Center, the Philadelphia Police Department. He oversees a team of analysts that conducts strategic crime analysis and . Matthew Lattanzio, Analyst Matthew has worked for the Philadelphia Police Department for 6 years. His background includes investigative support at the Real-Time Crime Center, a variety of quantitative crime analysis, and application development. Kevin Thomas, Director of Research and Analysis Kevin is the Director of Research and Analysis Unit where he oversees GIS, Statistics, and Analysis sections that conduct both tactical and strategic analysis. R&A also centrally warehouses a variety of data sources across the department. R&A also developed a web-based link analysis application by leveraging the centrally managed databases.
Social networks are ubiquitous in the world we live in. From protein and neural networks in our body to online social networks like Facebook and Twitter, the analysis of networks is an emerging area of great importance. In this talk, we look at the fundamental tenets of social network analysis, and some associated visualizations using Gephi.
Views: 494 TeachEdison
Social Network Analysis (SNA)  by Alvin Soleh, KMPlus.
KM & Innovation by Alvin Soleh, KMPlus.
Views: 40 Alvin Soleh
Social Network Analysis for Knowledge Management
In an interview with Alakh Asthana of eClerx Services, Verna Allee talks about social network analysis (SNA) and the impact of SNA on knowledge management (KM). Verna starts off by explaining that while SNA has been around since the 1930's, it's only in the last few years that an increasing number of organizations have started using SNA graphs to identify individual connectors and knowledge flows facilitated by people. Knowledge management has successfully adopted SNA for identifying experts and quick decision making. Verna recommends books in the area of SNA by Rob Cross and Patti Anklam, who have done substantial amount of work in the area organizational network analysis. On the implementation front, Verna advises that just because an employee is well networked, he / she should not be judged as a better or worse employee. She cautions that companies are often inclined to use social network graphs for performance evaluation and that is potentially a very dangerous thing to do. Verna sums up the interview by highlighting two key uses of SNA - help locate expertise and understand knowledge flows.
Views: 1466 eClerxServices
Penerapan SNA (Social Network Analysis) daalam Business - Studi Kasus Snapchat
E-Culture Nurul Amaliya 1401144321 MB-38-02
Views: 14 Nurul Amaliya
Social Network Analysis Using R Programming Language / Analyzing Social Networks /Learn R
This video shows how to use SNA package to analyze social networks in R programming language. Learn the basics of R language and try data science! Ram Subramaniam Stanford
Views: 100488 Ram Subramaniam
What is SNA using qualitative methods? by Nick Crossley and Gemma Edwards
How to use mixed methods in Social Network Analysis For more methods resources see: http://www.methods.manchester.ac.uk
Views: 1792 methodsMcr
An Introduction to Social Network Analysis: Part 1
Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given. Some research examples are provided. Concepts, substantive topics, and theories include social capital, Granovetter’s weak ties argument, Small World Studies, Burt’s structural holes argument, the application of SNA to collective action and social movements, amongst others.
Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So what then is “closeness” or “betweenness” in a network? How do we figure these things out and how do we interpret them? This video is part of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
SPI Social Network Analysis Webinar
The Smart Policing Initiative presented a webinar on "Social Network Analysis" on October 28, 2013 from 2:00 pm to 3:30 pm Eastern Time. Dr. Michael D. White, Dr. Charles M. Katz, and doctoral student David Choate of Arizona State University facilitated this webinar. Members of the Kansas City, MO SPI team also shared their experiences with using social network analysis as part of their SPI project. The webinar provides an introduction to social network analysis (SNA) and its applicability for law enforcement. The webinar describes the basic tenets of SNA and defines key concepts. It provides examples of social network analysis and how it is currently being used in SPI sites. The webinar concludes with a discussion of how SPI sites can start to use SNA in their own communities. For more information, including suggested readings to accompany the webinar, visit the SPI website: http://www.smartpolicinginitiative.com/tta/social-network-analysis-webinar-october-2013
Introduction to Social Network Analysis with Sentinel Visualizer
Learn how the Sentinel Visualizer software program uses Social Network Analysis (SNA) to find the most central players in any network using a variety of metrics. Find hidden relationships among people, places, things, and events. Use SNA metrics like Betweenness, Closeness, Degree, Centrality Eigenvalue, Hub, and Authority to visualize the hubs, spokes, and powerful people who span cells. For more information on SNA, visit: http://www.fmsasg.com/SocialNetworkAnalysis
Views: 6559 FMSChannel
Social Network Analysis
This webinar provides an introductory glance at how social network analysis (SNA) can be used to support law enforcement and community policing initiatives. It begins with an overview of basic social network concepts, demonstrates the development of several network mapping tools for describing, identifying, and evaluating group member involvement in criminal activity, and provides insights into how social network analytics can be used to strengthen collaborations among community organizations and service providers. Learning objectives include: (1) understanding key social network concepts, (2) appreciating how SNA can inform existing community policing efforts, and (3) being able to strategize ways to incorporate SNA into evaluation plans for grant-funded policing and community development initiatives.
Social Network Analysis Project Report
Here, is the video for our SNA project. Different Rounds are explained by different teammates. Topics covered: Centrality Measures (Aditya Gupta), Random Models (Kshitiz Sharma), Information Cascading (Shreyans Sureja), & Comunity Detection (Saloni Goyal).
Views: 14 Kshitiz Sharma
What is DYNAMIC NETWORK ANALYSIS? What does DYNAMIC NETWORK ANALYSIS mean? DYNAMIC NETWORK ANALYSIS meaning - DYNAMIC NETWORK ANALYSIS definition - DYNAMIC NETWORK ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Dynamic network analysis (DNA) is an emergent scientific field that brings together traditional social network analysis (SNA), link analysis (LA), social simulation and multi-agent systems (MAS) within network science and network theory. There are two aspects of this field. The first is the statistical analysis of DNA data. The second is the utilization of simulation to address issues of network dynamics. DNA networks vary from traditional social networks in that they are larger, dynamic, multi-mode, multi-plex networks, and may contain varying levels of uncertainty. The main difference of DNA to SNA is that DNA takes interactions of social features conditioning structure and behavior of networks into account. DNA is tied to temporal analysis but temporal analysis is not necessarily tied to DNA, as changes in networks sometimes result from external factors which are independent of social features found in networks. One of the most notable and earliest of cases in the use of DNA is in Sampson's monastery study, where he took snapshots of the same network from different intervals and observed and analyzed the evolution of the network. DNA statistical tools are generally optimized for large-scale networks and admit the analysis of multiple networks simultaneously in which, there are multiple types of nodes (multi-node) and multiple types of links (multi-plex). Multi-node multi-plex networks are generally referred to as meta-networks or high-dimensional networks. In contrast, SNA statistical tools focus on single or at most two mode data and facilitate the analysis of only one type of link at a time. DNA statistical tools tend to provide more measures to the user, because they have measures that use data drawn from multiple networks simultaneously. Latent space models (Sarkar and Moore, 2005) and agent-based simulation are often used to examine dynamic social networks (Carley et al., 2009). From a computer simulation perspective, nodes in DNA are like atoms in quantum theory, nodes can be, though need not be, treated as probabilistic. Whereas nodes in a traditional SNA model are static, nodes in a DNA model have the ability to learn. Properties change over time; nodes can adapt: A company's employees can learn new skills and increase their value to the network; or, capture one terrorist and three more are forced to improvise. Change propagates from one node to the next and so on. DNA adds the element of a network's evolution and considers the circumstances under which change is likely to occur. There are three main features to dynamic network analysis that distinguish it from standard social network analysis. First, rather than just using social networks, DNA looks at meta-networks. Second, agent-based modeling and other forms of simulations are often used to explore how networks evolve and adapt as well as the impact of interventions on those networks. Third, the links in the network are not binary; in fact, in many cases they represent the probability that there is a link. A meta-network is a multi-mode, multi-link, multi-level network. Multi-mode means that there are many types of nodes; e.g., nodes people and locations. Multi-link means that there are many types of links; e.g., friendship and advice. Multi-level means that some nodes may be members of other nodes, such as a network composed of people and organizations and one of the links is who is a member of which organization. While different researchers use different modes, common modes reflect who, what, when, where, why and how. A simple example of a meta-network is the PCANS formulation with people, tasks, and resources. A more detailed formulation considers people, tasks, resources, knowledge, and organizations. The ORA tool was developed to support meta-network analysis.
Views: 832 The Audiopedia
On Call: Social Network Analysis (SNA) as a Quality Improvement Measurement Tool
Synopsis: How can quality improvement (QI) relationships and networks be visualized and measured? How can QI teams know they have engaged the right people and organizations, as well as identified key people who can help with the uptake and spread of a QI initiative? How can QI uptake and spread be measured? Social network analysis (SNA) is a method that models and measures relationships between people and groups, and it can help answer these questions. This webinar will provide an introduction to SNA – including how to collect data and interpret a basic social network map. Demonstrating the application of SNA in QI work, CFHI will provide an overview of how this method is being applied as an evaluative tool within a regional collaboration, including early results from the Atlantic Healthcare Collaboration. After this webinar, participants will be able to: Describe the general theory behind social network analysis; Collect social network data; Create a social network visualization; and Interpret social network data.
First Steps to NetViz Nirvana: Evaluating Social Network Analysis with NodeXL
Social Network Analysis (SNA) has evolved as a popular, standard method for modeling meaningful, often hidden structural relationships in communities. Existing SNA tools often involve extensive pre-processing or intensive programming skills that can challenge practitioners and students alike. NodeXL, an open-source template for Microsoft Excel, offers a potentially low-barrier-to-entry framework for teaching and learning SNA. We present the findings of 2 user studies of 21 graduate students who engaged in SNA using NodeXL. We found NodeXL to be an effective tool for a diverse set of users, and significantly, a tightly integrated metrics/visualization tool that can spark insight and facilitate sense-making for students of SNA. Our presentation will focus on the unique features that made NodeXL learnable and usable. After a brief overview of the NodeXL tool, we will describe our research methodology, based on Multi-dimensional In-depth Long-term Case studies (MILCs), an approach that enables effective evaluations of complex visual analytics tools. We will discuss NetViz Nirvana, layout principles that can increase the readability and interpretative power of social network visualizations, and present a sample of visualizations produced by the students. Finally, we will offer lessons learned for educators, researchers, and developers of SNA tools such as NodeXL.
Views: 168 Microsoft Research
Comm 606: Social Network Analysis Presentation
Presentation of SNA for Comm 606 at WVU. As someone who works in a "self-owned business" standpoint with a blog/video blog and graphic design company, I utilize these as my workplace in examples of SNA and how it can be used. For an interesting, nerdy exploration of SNA and the details of one of the graphs and maps included, check out the "Star Wars social networks" here: http://evelinag.com/blog/2016/01-25-social-network-force-awakens/#.XKBdSNF7mL8 Royalty-free music from bensound.com
Views: 22 Trinity Mullins
Cara Menggunakan Tool UCINET dan NETDRAW Metode Social Network Analysis (SNA)
langkah-langkah menggunakan tool UCINET dan NETDRAW untuk visualisasi data dalam bentuk graff. semoga bermanfaat. terimakasih
Social Network Analysis
Elizabeth Pyatt, instructional designer for TLT, talks about SNA (Social Network Analysis), which looks at how people are connected into communities.
Views: 57 Penn State TLT
Network Structure
An introduction to social network analysis and network structure measures, like density and centrality. Table of Contents: 00:00 - Network Structure 00:12 - Degree Distribution 02:42 - Degree Distribution 06:17 - Density 10:31 - Clustering Coefficient 11:24 - Which Node is Most Important? 12:10 - Which Node is Most Important? 13:27 - Closeness Centrality 15:01 - Closeness Centrality 16:17 - Closeness Centrality 16:36 - Degree Centrality 17:33 - Betweenness Centrality 17:53 - Betweenness Centrality 20:55 - Eigenvector Centrality 23:02 - Connectivity and Cohesion 24:24 - Small Worlds 26:28 - Random Graphs and Small Worlds
Views: 67598 jengolbeck
Rob Chew, Peter Baumgartner | Connected: A Social Network Analysis Tutorial with NetworkX
PyData Carolinas 2016 Social Network Analysis (SNA), the study of the relational structure between actors, is used throughout the social and natural sciences to discover insight from connected entities. In this tutorial, you will learn how to use the NetworkX library to analyze network data in Python, emphasizing intuition over theory. Methods will be illustrated using a dataset of the romantic relationships between characters on "Grey's Anatomy", an American medical drama on the ABC television network. Analysis and intuition will be emphasized over theory and mathematical rigor. An IPython/Jupyter notebook format will be used as we code through the examples together.
Views: 5666 PyData
SNA introduction!!!
introduction to the animation team named stick nation animation. the link i talked about! http://www.youtube.com/watch?v=C44dDlqpw0I
Introduction to SNA. Lecture 1. Introduction to Network Science
Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture1.pdf Introduction to network science. Examples.
Views: 4143 Leonid Zhukov
Introduction to SNA. Lecture 4. Node centrality and ranking on networks.
Node centrality metrics, degree centrality, closeness centrality, betweenness centrality, eigenvector centrality. Status and rank prestige, PageRank,Hubs and Authorities. Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture4.pdf
Views: 2128 Leonid Zhukov