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What is Social Network Analysis?
 
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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?
Social Network Analysis Overview
 
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For full courses see: https://goo.gl/JJHcsw Follow along with the course eBook: https://goo.gl/Z2ekrB A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF Transcription: Social network analysis is the application of network theory to the modeling and analysis of social systems. it combine both tools for analyzing social relations and theory for explaining the structures that emerge from the social interactions. Of course the idea of studying societies as networks is not a new one but with the rise in computation and the emergence of a mass of new data sources, social network analysis is beginning to be applied to all type and scales of social systems from, international politics to local communities and everything in between. Traditionally when studying societies we think of them as composed of various types of individuals and organizations, we then proceed to analysis the properties to these social entities such as their age, occupation or population, and them ascribe quantitative value to them. This allows social science to use the formal mathematical language of statistical analyst to compare the values of these properties and create categories such as low in come house holds or generation x, we then search for quasi cause and effect relations that govern these values. This component-based analysis is a powerful method for describing social systems. Unfortunately though is fails to capture the most important feature of social reality that is the relations between individuals, statistical analysis present a picture of individuals and groups isolates from the nexus of social relations that given them context. Thus we can only get so far by studying the individual because when individuals interact and organize, the results can be greater than the simple sum of its parts, it is the relations between individuals that create the emergent property of social institutions and thus to understand these institutions we need to understand the networks of social relations that constitute them. Ever since the emergence of human beans we have been building social networks, we live our lives embed in networks of relations, the shape of these structures and where we lie in them all effect our identity and perception of the world. A social network is a system made up of a set of social actors such as individuals or organizations and a set of ties between these actors that might be relations of friendship, work colleagues or family. Social network science then analyze empirical data and develops theories to explaining the patterns observed in these networks In so doing we can begin to ask questions about the degree of connectivity within a network, its over all structure, how fast something will diffuse and propagate through it or the Influence of a given node within the network. lets take some examples of this Social network analysis has been used to study the structure of influence within corporations, where traditionally we see organization of this kind as hierarchies, by modeling the actual flow of information and communication as a network we get a very different picture, where seemingly irrelevant employees within the hierarchy can in fact have significant influence within the network. Researcher also study innovation as a process of diffusion of new ideas across networks, where the oval structure to the network, its degree of connectivity, centralization or decentralization are a defining feature in the way that innovation spreads or fails to spread. Network dynamics, that is how networks evolve overtime is another important area of research, for example within Law enforcement agencies social network analysis is used to study the change in structure of terrorists groups to identify changing relations through which they are created, strengthened and dissolved? Social network analysis has also been used to study patterns of segregation and clustering within international politics and culture, by mapping out the beliefs and values of countries and cultures as networks we can identify where opinions and beliefs overlap or conflict. Social network analysis is a powerful new method we now have that allows us to convert often large and dense data sets into engaging visualization, that can quickly and effectively communicate the underlining dynamics within the system. By combine new discoveries in the mathematics of network theory, with new data sources and our sociological understanding, social network analysis is offering huge potential for a deeper, richer and more accurate understanding, of the complex social systems that make up our world.
Views: 37422 Complexity Labs
Basics of Social Network Analysis
 
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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: 34358 Alexandra Ott
Network Analysis Tutorial: Introduction to Networks
 
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This is the first video of chapter 1 of Network Analysis by Eric Ma. Take Eric's course: https://www.datacamp.com/courses/network-analysis-in-python-part-1 From online social networks such as Facebook and Twitter to transportation networks such as bike sharing systems, networks are everywhere, and knowing how to analyze this type of data will open up a new world of possibilities for you as a Data Scientist. This course will equip you with the skills to analyze, visualize, and make sense of networks. You'll apply the concepts you learn to real-world network data using the powerful NetworkX library. With the knowledge gained in this course, you'll develop your network thinking skills and be able to start looking at your data with a fresh perspective! Transcript: Hi! My name is Eric, and I am a Data Scientist working at the intersection of biological network science and infectious disease, and I'm thrilled to share with you my knowledge on how to do network analytics. I hope we'll have a fun time together! Let me first ask you a question: what are some examples of networks? Well, one example might be a social network! In a social network, we are modelling the relationships between people. Here’s another one - transportation networks. In a transportation network, we are modelling the connectivity between locations, as determined by roads or flight paths connecting them. At its core, networks are a useful tool for modelling relationships between entities. By modelling your data as a network, you can end up gaining insight into what entities (or nodes) are important, such as broadcasters or influencers in a social network. Additionally, you can start to think about optimizing transportation between cities. Finally, you can leverage the network structure to find communities in the network. Let’s go a bit more technical. Networks are described by two sets of items: nodes and edges. Together, these form a “network”, otherwise known in mathematical terms as a “graph”. Nodes and edges can have metadata associated with them. For example, let’s say there are two friends, Hugo and myself, who met on the 21st of May, 2016. In this case, the nodes may be “Hugo” and myself, with metadata stored in a key-value pair as “id” and “age”. The friendship is represented as a line between the two nodes, and may have metadata such as “date”, which represents the date on which we first met. In the Python world, there is a library called NetworkX that allows us to manipulate, analyze and model graph data. Let’s see how we can use the NetworkX API to analyze graph data in memory. NetworkX is typically imported as nx. Using nx.Graph(), we can initialize an empty graph to which we can add nodes and edges. I can add in the integers 1, 2, and 3 as nodes, using the add_nodes_from() method, passing in the list [1, 2, 3] as an argument. The Graph object G has a .nodes() method that allows us to see what nodes are present in the graph, and returns a list of nodes. If we add an edge between the nodes 1 and 2, we can then use the G.edges() method to return a list of tuples which represent the edges, in which each tuple shows the nodes that are present on that edge. Metadata can be stored on the graph as well. For example, I can add to the node ‘1’ a ‘label’ key with the value ‘blue’, just as I would assign a value to the key of a dictionary. I can then retrieve the node list with the metadata attached using G.nodes(), passing in the data=True argument. What this returns is a list of 2-tuples, in which the first element of each tuple is the node, and the second element is a dictionary in which the key-value pairs correspond to my metadata. NetworkX also provides basic drawing functionality, using the nx.draw() function. nx.draw() takes in a graph G as an argument. In the IPython shell, you will also have to call the plt.show() function in order to display the graph to screen. With this graph, the nx.draw() function will draw to screen what we call a node-link diagram rendering of the graph. The first set of exercises we’ll be doing here is essentially exploratory data analysis on graphs. Alright, let’s go on and take a look at the exercises! https://www.datacamp.com/courses/network-analysis-in-python-part-1
Views: 18020 DataCamp
Network Analysis. Lecture 1. Introduction to Network Science
 
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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: 20402 Leonid Zhukov
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 3418 Microsoft Research
Nicholas Christakis: The Sociological Science Behind Social Networks and Social Influence
 
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If You're So Free, Why Do You Follow Others? The Sociological Science Behind Social Networks and Social Influence. Nicholas Christakis, Professor of Medical Sociology, Medicine, and Sociology at Harvard University If you think you're in complete control of your destiny or even your own actions, you're wrong. Every choice you make, every behavior you exhibit, and even every desire you have finds its roots in the social universe. Nicholas Christakis explains why individual actions are inextricably linked to sociological pressures; whether you're absorbing altruism performed by someone you'll never meet or deciding to jump off the Golden Gate Bridge, collective phenomena affect every aspect of your life. By the end of the lecture Christakis has revealed a startling new way to understand the world that ranks sociology as one of the most vitally important social sciences. The Floating University Originally released September 2011. Additional Lectures: Michio Kaku: The Universe in a Nutshell http://www.youtube.com/watch?v=0NbBjNiw4tk Joel Cohen: An Introduction to Demography (Malthus Miffed: Are People the Problem?) http://www.youtube.com/watch?v=2vr44C_G0-o Steven Pinker: Linguistics as a Window to Understanding the Brain http://www.youtube.com/watch?v=Q-B_ONJIEcE Leon Botstein: Art Now (Aesthetics Across Music, Painting, Architecture, Movies, and More.) http://www.youtube.com/watch?v=j6F-sHhmfrY Tamar Gendler: An Introduction to the Philosophy of Politics and Economics http://www.youtube.com/watch?v=mm8asJxdcds
Views: 251140 Big Think
Social Network Analysis
 
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Mathematica provides state-of-the-art functionality for analyzing and synthesizing graphs and networks. One application of the new functionality is social network analysis. In this talk from the Wolfram Technology Conference 2011, Charles Pooh, a Senior Kernel Developer at Wolfram Research, explains the background of network analysis and basic concepts of network analysis with Mathematica. For more information about Mathematica, please visit: http://www.wolfram.com/mathematica
Views: 6615 Wolfram
Network Analysis. Lecture 18. Link prediction.
 
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Link prediction problem. Proximity measures. Scoring algorithms. Prediction by supervised learning. Performance evaluation. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture18.pdf
Views: 4347 Leonid Zhukov
Social Network Analysis
 
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Views: 1244 Wolfram
Social Network Analysis with R | Examples
 
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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: 14168 Bharatendra Rai
A Quick Look at Social Network Analysis
 
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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?
An Introduction to Social Network Analysis: Part 1
 
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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.
The Basics of Social Network Analysis: A Social Network Lab in R for Beginners
 
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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
What is Social Network Analysis? by Martin Everett
 
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The focus of social network analysis is on the network of relations. A social network consists of a set of actors (also called nodes or vertices) together with a set of edges (also called arcs) that link pairs of actors. Since edges can share actors (e.g., the A.B edge shares an actor with the B.C edge) this creates a connected web that we think of as a network. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 311 methodsMcr
Network Analysis. Lecture 5. Centrality measures.
 
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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: 15120 Leonid Zhukov
Using (Excel) .NetMap for Social Network Analysis
 
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This free, online event was held on October 27, 2008, and was convened by the Ash Center's Government Innovators Network. Visit the Government Innovators Network to view the slide presentations related to this event, for information on future webinars, and to explore innovations in government at www.innovations.harvard.edu. Event description: (Excel) .NetMap is an add-in for Office 2007 that provides social network diagram and analysis tools in the context of a spreadsheet. Adding the directed graph chart type to Excel opens up many possibilities for easily manipulating networks and controlling their display properties. This session provided a walk-through of the basic operation of .NetMap. This tutorial was conducted by Marc Smith with an introduction by David Lazer.
Views: 16343 Harvard Ash Center
Network Analysis. Lecture 13. Epidemics on networks
 
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Spread of epidemics on networks.SI, SIS, SIR models. Epidemic threshold. Simulation of infection propagation. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture13.pdf
Views: 2078 Leonid Zhukov
UCCSS Hilbert SNA1: Social Network Analysis - network structure
 
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This lecture is part of the University of California wide online course on Computational Social Science (UCCSS), produced with input from Professors from all 10 UC campuses and offered to UC students for credit since 2018.
Views: 94 Martin Hilbert
Social Network Analysis Using R Programming Language / Analyzing Social Networks /Learn R
 
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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: 73882 Ram Subramaniam
Introduction to Social Network Analysis
 
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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.
Directed Network Analysis - Simulating a Social Network Using Networkx in Python - Tutorial 28
 
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In this Tutorial on Python for Data Science, you will learn how to Simulate a social network and how to do network analysis using Networkx in python. there are 3 easy steps to do it. generate graph objects and edge list, assign graph attributes to graph nodes, and visualize the network. This is the 28th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
Views: 2961 TheEngineeringWorld
Social Networks: a Basic Introduction in Less Than 3 Minutes
 
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Social Networks: A Basic Introduction in Four Minutes 1. Two Elements in Networks Tie: Ties represent relations between nodes Node: Nodes represent things that relate somehow to one another 2. Two Kinds of Networks Directed Networks: Networks in which the tie has direction (AKA "digraphs"). Examples: hitting, kissing, infecting, sending letters. Undirected Networks: Networks in which the tie has no direction. Examples: playing tennis with, married to, codefendant 3. Distance: "Geodesic" (Shortest Path) 4. Network Density Directed Networks: # actual ties / (n*(n-1)) Undirected Networks: # actual ties / (n*(n-1))/2 where n=# of nodes 4. Closeness Centrality: = 1/Farness Farness: Sum of Distance to all other Nodes 5. Degree centrality: how many ties touch a node? 6. Betweenness Centrality for Node X: Sum for all pairS of nodes (of the fraction of geodesics between A pair of nodes that have Node X in the middle) 7. Ego Networks Level 1.0: Ego's Ties to Alters Level 1.5: Level 1.0 Plus Alters' Ties to Other Alters Level 2.0: Level 1.5 Plus Alters' Ties to Alters' Alters 8. Induced Homophily: A tendency for ties to form to similar others because similar others are especially present in the social environment (group, community, society) Example: No wonder blues are mostly tied to blues... there are hardly any reds out there! 9. Choice Homophily: A tendency to choose to form ties with similar others even when different others are available in the social environment (group, community, society) Lots of blues and lots of reds out there, Yet each is mostly tied to its own kind! 10. Kozo Sugiyama's Network Design Principles in the abstract • Ties should be easy to follow from node to node • Ties should be far from one another • Ties should not cross or touch • Ties should be straight • Nodes that connect should be close • Similar nodes should be close • Central nodes should be in the center Sugiyama, Kozo. 2002. Graph Drawing and Applications for Software and Knowledge Engineers. Singapore: World Publishing Company Inc. That's easy to do in small, abstract networks... ... but large, real-world networks pose a challenge. 11. Looking for elaboration? Looking for explanation? Looking for application? Looking for more? Check out http://bit.ly/1M4RBEE Undergraduate Social Networks at uma.edu
Views: 5365 James Cook
10 Ego-Network Analysis, 2018
 
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2018 Social Networks and Health Workshop, Brea Perry, Professor of Sociology, Indiana University Network Science Institute, 05/15/2018
Views: 113 Duke SSRI
Dynamic Social Network Analysis: Model, Algorithm, Theory, & Application CMU Research Speaker Series
 
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Across the sciences, a fundamental setting for representing and interpreting information about entities, the structure and organization of communities, and changes in these over time, is a stochastic network that is topologically rewiring and semantically evolving over time, or over a genealogy. While there is a rich literature in modeling invariant networks, until recently, little has been done toward modeling the dynamic processes underlying rewiring networks, and on recovering such networks when they are not observable. In this talk, I will present some recent developments in analyzing what we refer to as the dynamic tomography of evolving networks. I will first present a new class of statistical models known as dynamic exponential random graph models for evolving social networks, which offers both good statistical property and rich expressivity; then, I will present new sparse-coding algorithms for estimating the topological structures of latent evolving networks underlying nonstationary time-series or tree-series of nodal attributes, along with theoretical results on the asymptotic sparsistency of the proposed methods; finally, I will present a new Bayesian model for estimating and visualizing the trajectories of latent multi-functionality of nodal states in the evolving networks. I will show some promising empirical results on recovering and analyzing the latent evolving social networks in the US Senate and the Enron Corporation at a time resolution only limited by sample frequency. In all cases, our methods reveal interesting dynamic patterns in the networks.
Views: 2636 Microsoft Research
Webinar Series | Social Network Analysis: An Innovative Tool to Maximize NIBIN Lead
 
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Find out how NIBIN leads can be used to gain insight into how violent incidents can be connected through a common gun and how to link incident data to the individuals involved in those crimes.
Social Network Analysis on Digital Marketing
 
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EEL 6935 Network Science and Application - Social Network Analysis and Its Application on Digital Marketing and Decision Making • Analyzed social network and the potential influence that the social network may impact on decision making • Detected the sources of complex social network by applying Susceptible In (SI) model using network centrality • Promoted the designed paradigm for a new promising digital marketing approach to efficiently target the right group
Views: 398 Bowen Zhang
What is Social Network Analysis? by Prof Martin Everett
 
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The focus of social network analysis is on the network of relations. A social network consists of a set of actors (also called nodes or vertices) together with a set of edges (also called arcs) that link pairs of actors. Since edges can share actors (e.g., the A.B edge shares an actor with the B.C edge) this creates a connected web that we think of as a network. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 30723 methodsMcr
Social Network Analysis
 
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Idiros Introduction to Social Network Analysis
Views: 1192 Idiro Analytics
Social Network Analysis using python
 
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Capstone project of Data and Knowledge Engineering Repository: https://github.com/Prerna237/SocialNetworkAnalysis
Views: 2152 PRERNA 
Rob Chew, Peter Baumgartner | Connected: A Social Network Analysis Tutorial with NetworkX
 
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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: 4070 PyData
Clustering in Social Network Analysis: A Social Network Lab in R for Beginners
 
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DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ What is clustering or degree distribution, and how do they affect our interpretation of what’s going on in a network? We define these terms in this video. 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
Social Network Analysis
 
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Brief info on how to conduct Social Network Analysis (SNA)
Views: 5431 KMPlus Consulting
Social network analysis: Considerations for data collection and analysis
 
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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.
Network Analysis. Lecture 7. Structural Equivalence and Assortative Mixing
 
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Structural and regular equivalence. Similarity metrics. Correlation coefficient and cosine similarity. Assortative mixing and homophily. Modularity. Assortativity coefficient. Mixing by node degree. Assortative and disassortative networks Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture7.pdf
Views: 3662 Leonid Zhukov
Statistical Modeling of Social Networks
 
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1/28/2013 Mark Handcock - UCLA "Statistical Modeling of Social Networks" "In this talk we give an overview of social network analysis from the perspective of a statistician. The networks field is, and has been, broadly multidisciplinary with significant contributions from the social, natural and mathematical sciences. This has lead to a plethora of terminology, and network conceptualizations commensurate with the varied objectives of network analysis. As the primary focus of the social sciences has been the representation of social relations with the objective of understanding social structure, social scientists have been central to this development. We illustrate these ideas with Exponential-family random graph models (ERGM) which attempt to represent the complex dependencies in networks in a parsimonious, tractable and interpretable way. A major barrier to the application of such models has been lack of understanding of model behavior and a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the network structure produced by the models matches that of the data. We will also consider latent cluster random effects models and touch upon issues of the sampling of networks and partially-observed networks. We illustrate these methods using the "statnet" open-source software suite (http://statnet.org)."
Views: 3779 UCLABEC
Social Network Analysis, Behavioral Research on HIV/AIDS, UCLA
 
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An Introduction to Applying Social Network Analysis to Behavioral Research on HIV/AIDS.
Views: 6313 UCLA
Enabling qualitative social network analysis
 
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Social network analysis is often thought of as quantitative having its roots in mathematics graph theory. However, that is overlooking the tradition of many qualitative researchers who focus on looking at relationships and interactions among people and within communities. In fact, anthropologists at the University of Manchester (UK) in the 1960s played a major role in the development of qualitative social network analysis. However, the tools that have developed to manage such data have mainly focussed on visualizing networks and/or providing statistical analysis. NVivo 11 Plus’s sociogram tool enables both the visualization of networks as well as drilling easily into the qualitative context. This webinar looks at: • What is qualitative social network analysis? • Qualitative and mixed methods techniques for collecting social network data • How NVivo 11 Plus supports the analysis of qualitative and mixed methods approaches to social network analysis http://www.qsrinternational.com/product/NVivo11-for-Windows/Plus http://www.qsrinternational.com/product/NVivo11-for-Windows/Visualizations#New-Sociograms
Views: 1589 NVivo by QSR
Social Network Science
 
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See the full course: https://goo.gl/SyDv4i Follow along with the course eBook: https://goo.gl/DhMjNL In this module we will be taking a very high-level view to social network analysis, we talk about how connectivity creates a certain type of space, or what we call a topology that stretches and distorts our traditional conception of linear space. We discuss how reasoning about the general forces that are acting on the network can help us in providing some overall context to our analysis. We then go on to talk about some of the primary considerations to a social network's overall makeup and social cohesion, touching upon the topics of network density, clustering, average path length and degree distribution. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 1642 Complexity Labs
Closeness Centrality & Betweenness Centrality: A Social Network Lab in R for Beginners
 
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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
Complexity Science I Complexity Theory I Systems Theory I Social Network Analysis 2016
 
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Complexity Science I Complexity Theory I Systems Theory I Social Network Analysis 2016
Views: 1654 Jurgen Atina
3. CYTOSCAPE ESSENTIALS: network analysis, colouring edges, making edges dissapear
 
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The CYTOSCAPE HELPDESK google group is a very useful source for any more detailed information in regard to Cytoscape usage
Views: 7707 Marko Radulovic
Awesometrics Social Network Analysis: Mention Network
 
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SNA graph based on mention network between Twitter user. Case: @aniesbaswedan and @susipudjiastuti.
Views: 387 Awesometrics
Social network analysis - Introduction to structural thinking: Dr Bernie Hogan, University of Oxford
 
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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
Mini Lecture: Social Network Analysis for Fraud Detection
 
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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: 14352 Bart Baesens

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