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?
Views: 31933 Mod•U: Powerful Concepts in Social Science
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: 40462 Alexandra Ott
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: 23645 Bharatendra Rai
langkah-langkah menggunakan tool UCINET dan NETDRAW untuk visualisasi data dalam bentuk graff. semoga bermanfaat. terimakasih
Views: 114 Mariani Rospilinda Siki
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: 18207 Leonid Zhukov
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: 468 TeachEdison
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: 22798 Fullstack Academy
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
Views: 16040 Mod•U: Powerful Concepts in Social Science
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
Views: 672 IBM Security Support
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.
Views: 273 Justice Research and Statistics Association
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: 30594 Systems Academy
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: 14972 Bart Baesens
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: 2730 Leonid Zhukov
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: 80162 Ram Subramaniam
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/
Views: 9 ESRC BLG Data Research Centre
Data mining algorithms are focused on finding frequently occurring patterns in historical data. These techniques are useful in many domains, but for fraud detection it is exactly the opposite. Rather than being a pattern repeatedly popping up in a data set, fraud is an uncommon, well-considered, imperceptibly concealed, time-evolving and often carefully organized crime which appears in many types and forms. As traditional techniques often fail to identify fraudulent behavior, social network analysis offers new insights in the propagation of fraud through a network. Indeed, fraud is not something an individual would commit by himself, but is often organized by groups of people loosely connected to each other. The use of networked data in fraud detection becomes increasingly important to uncover fraudulent patterns and to detect in real-time when certain processes show some characteristics of irregular activities. Although analyses focus in the first place on fraud detection, the emphasis should shift towards fraud prevention, i.e. detecting fraud before it is even committed. As fraud is a time-evolving phenomenon, social network algorithms succeed to keep ahead of new types of fraud and to adapt to changing environment and surrounding effects.
Views: 9308 Bart Baesens
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.
Views: 761 Social Sciences Research Laboratories (SSRL)
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
Views: 11139 International Journalism Festival
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
Views: 4868 Mod•U: Powerful Concepts in Social Science
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.
Views: 121 Justice Research and Statistics Association
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: 1605 Leonid Zhukov
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.
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: 65850 jengolbeck
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.
Views: 337 Social Sciences Research Laboratories (SSRL)
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: 13582 James Cook
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
Views: 693 Strategies for Policing Innovation
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: 1456 eClerxServices
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: 5287 PyData
Introduction to network science. Complex networks. Examples. Main properties. Scale-free networks. Small world. Six degrees of separation. Milgram study. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture1.pdf
Views: 23648 Leonid Zhukov
Across the U.S., community-based hepatitis B coalitions are working to reduce the health disparities associated with hepatitis B by increasing awareness, screening, vaccination, and linkage to care for high-risk communities. Coalition success depends on collaboration, from engaging members to sharing promising practices and integrating efforts to maximize time and resources. The George Washington University (GW) Cancer Center used social network analysis (SNA) to improve program delivery and monitor the development of relationships between Community Roundtable participants working to integrate cancer and chronic disease efforts. Join this session to learn how coalitions could leverage SNA to inform program and coalition improvement, provide data that supports anecdotes and demonstrate the development of professional relationships.
Views: 27 Hep B United
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: 6527 FMSChannel
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: 12 Kshitiz Sharma
E-Culture Nurul Amaliya 1401144321 MB-38-02
Views: 14 Nurul Amaliya
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: 1994 Leonid Zhukov
Bernie Hogan completed his BA(hons) at the Memorial University of Newfoundland in Canada, where he received the University Medal in Sociology. Since then he has been working on Internet use and social networks at the University of Toronto under social network analysis pioneer Barry Wellman. Bernie received his Masters of Arts at Toronto in 2003, and defended his PhD Dissertation in the Fall of 2008. His dissertation examines how the use of ICTs alters the way people maintain their relationships in everyday life. In 2005 he was an intern at Microsoft’s Community Technologies Lab, working with Danyel Fisher on new models for email management. RESEARCH Bernie Hogan’s research focuses on the creation, maintenance and analysis of personal social networks, with a particular focus on the relation between online and offline networks. Hogan’s work has demonstrated the utility of visualization for network members, how the addition of new social media can complicate communication strategies, and how the uneven distribution of media globally can affect the ability of people to participate online. Currently, Hogan is working on techniques to simplify the deployment of personal network studies for newcomers as well as social-theoretical work on the relationship between naming conventions and identities. #datascienceclasses
Views: 1308 The Alan Turing Institute
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: 783 The Audiopedia
This talk will focus on How SNA can help enhance the outcomes of Marketing Campaigns by using social network graphs . Social network analytics (SNA) is the process of investigating social structures through the use of network and graph theories. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties or edges (relationships or interactions) that connect them. This is emerging as an important tool to understand customer behavior and influencing his behavior. The talk will focus on the mathematics behind SNA and how SNA can help make marketing decisions for telecom operators. SNA use case will use telecom consumer data to establish networks based on their calling behavior like frequency, duration of calls, types of connections and thus establish major communities and influencers. By identifying key influencers and active communities marketing campaigns can be made more effective/viral. It helps in improving the adoption rate by targeting influencers with a large degree of followers. It will also touch upon how SNA helps retention rate and spread the impact of marketing campaigns. The tools used for use case is SAS SNA and Node XL for demonstration purpose. It will show how SNA helps in lifting the impact of campaigns. This use case will illustrate a project focused on building a SNA model using a combination of demographic/firmographic variables for companies variables and Call frequency details. The dimensions like the company you work with, the place you stay, your professional experience and position, Industry Type etc. helps add a lot more value to the social network graph. With the right combination of the dimensions and problem at hand, in our case, it was more of marketing analytics we can identify the right influencers within a network. The more dimensions we add, the network gets stronger and more effective for running campaigns. Details: https://confengine.com/odsc-india-2018/proposal/7312/social-network-analytics-to-enhance-marketing-outcomes-in-telecom-sector Conference: https://india.odsc.com/
Views: 244 ConfEngine
KM & Innovation by Alvin Soleh, KMPlus.
Views: 38 Alvin Soleh
Presenter: Eric Smith, Manager, Monitoring Evaluation, and Learning Coady International Institute, St. Francis Xavier University How can relationships between people and groups be visualized and measured? Who are the influencers, connectors and brokers? How can does knowledge and innovation spread? Social network analysis (SNA) is a method that models and measures relationships between people and groups, and it can help answer these questions. In this webinar, Eric will provide an introduction to SNA—including how to collect data and interpret a basic social network map— based on a recent study of Coady’s South African alumni. He will also explore graduates’ recommendations on how to bring alumni together for greater impact—and hopes that webinar participants will share their insights into this important topic and that viewers will comment here. Eric Smith leads Coady's monitoring, evaluation, and learning. As a “data-nerd” he enjoys exploring how participatory quantitative and qualitative MEL methods can transform power and agency to empower people, organizations and partners. Eric joined Coady in 2016. Prior to his work at Coady, he was with Genuine Progress Index Atlantic and Canada's International Development Research Centre.
Views: 45 Coady Institute
K-core decomposition of networks. Diads and triads. Edge reciprocity. Frequent subgraphs. Network motifs. Assortative mixing. Network visualization. Forde directed layouts. Adjacency matrix ordering. Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture6.pdf
Views: 1478 Leonid Zhukov
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