Home
Search results “Information social network analysis”
What is Social Network Analysis?
 
03:46
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
 
04:45
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: 38373 Complexity Labs
Introduction to Social Networks
 
03:51
The network of friendships on Facebook, road connections, terrorist networks and disease spreading networks are today available as a graph G(V,E). Social Network Analysis involves discerning this graph data and making sense out of it. The course will revolve around the study of some well-known theories of social and information networks and their applications on real world datasets.
Views: 3492 Social Networks
O'Reilly Webcast: Social Network Analysis -- Finding communities and influencers
 
01:05:28
A follow-on to Analyzing Social Networks on Twitter, this webcast will concentrate on the social component of Twitter data rather then the questions of data gathering and decomposition. Using a predefined dataset, we will attempt to find communities of people on Twitter that express particular interests. We will also mine Twitter streams for cascades of information diffusion, and determine most influential individuals in these cascades. The webcast will contain an initial introduction to Social Network Analysis methods and metrics. About Maksim Tsvetovat: Maksim Tsvetovat is an interdisciplinary scientist, a software engineer, and a jazz musician. He has received his doctorate from Carnegie Mellon University in the field of Computation, Organizations and Society, concentrating on computational modeling of evolution of social networks, diffusion of information and attitudes, and emergence of collective intelligence. Currently, he teaches social network analysis at George Mason University. He is also a co-founder of DeepMile Networks, a startup company concentrating on mapping influence in social media. Maksim also teaches executive seminars in social network analysis, including "Social Networks for Startups" and "Understanding Social Media for Decisionmakers". Produced by: Yasmina Greco
Views: 4321 O'Reilly
Social Network Analysis
 
22:06
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: 6629 Wolfram
Social Networking in Plain English
 
01:48
A short explanation of social networking websites and why they are popular. This video introduces the basic ideas behind Social Networking. It focuses on the role of social networking in solving real-world problems. It teaches: • The role of people networks in business and personal life • The hidden nature of real-world people networks • How social networking sites reveal hidden connections • The basic features of social networking websites Need explainer videos for your classroom? This video is a sample from a unique video library that can be licensed for use by educators. http://CommonCraft.com/ Ready-made Videos and Downloadable Visuals for Educators http://ExplainerAcademy.com/ Learn how to turn your ideas into clear, understandable explanations and animated explainer videos with our online courses. http://ArtOfExplanation.com/ We wrote the book on explanation and how to make ideas easy to understand. SOCIAL: Twitter ► https://twitter.com/CommonCraft Facebook ► https://www.facebook.com/CommonCraft Email ►https://www.commoncraft.com/newsletter
Views: 1340884 Common Craft
Network theory - Marc Samet
 
03:31
View full lesson: http://ed.ted.com/lessons/what-facebook-and-the-flu-have-in-common-marc-samet From social media to massive financial institutions, we live within a web of networks. But how do they work? How does Googling a single word provide millions of results? Marc Samet investigates how these networks keep us connected and how they remain "alive." Lesson by Marc Samet, animation by Thinkmore Studios.
Views: 134996 TED-Ed
Visual Analysis of Social Networks
 
35:19
An introduction to information visualization, specifically network visualization techniques. Table of Contents: 00:10 - Information Visualization 01:06 - Challenger 02:56 - 04:44 - 05:37 - 05:49 - 06:08 - Main Idea 06:34 - Information Visualization 07:45 - Key Attributes 09:07 - Tasks in Info Vis 09:45 - Tasks in Info Vis 10:07 - How Vis Amplifies Cognition 12:50 - Network Visualization 13:15 - What is interesting about this network? 21:49 - What makes a good visualization? 23:15 - Is this a good visualization? 23:56 - What about this one? 24:56 - And this one? 25:29 - And finally, this one? 26:06 - Node Size and Color 28:33 - Node Size and Color 29:40 - Edge Weight 30:04 - Visualization Issues 31:05 - Example: Senate Voting Records 31:48 - Filtering 32:38 - Example: Senate Voting Records 32:43 - Filtering 32:46 - Examples 32:51 - Visualization Tools 34:19 - In Class Exercise
Views: 7171 jengolbeck
Social Network Diffusion
 
15:21
See the full course: https://goo.gl/SyDv4i Follow along with the course eBook: https://goo.gl/DhMjNL The study of network diffusion tries to capture the underlying mechanism of how events propagate through a complex network, whether the subject of interest is a virus spreading through some population, the spreading of some social movement, some new fashion or innovation or it may be a marketing message through an online social network. In this video we will be covering some of the primary considerations when looking at the process of diffusion including; how will the structure of the network effect that process? How fast will it spread, for example will we get tipping points? how can we enable or constrain this process? Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF
Views: 2193 Complexity Labs
Example of basic Social Network Analysis of Facebook friends
 
08:20
http://paddytherabbit.com/example-facebook-friends-analysis/ I am using the Louvain method method for community detection
Views: 5642 David Sherlock
Dynamic Social Network Analysis: Model, Algorithm, Theory, & Application CMU Research Speaker Series
 
47:48
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: 2739 Microsoft Research
Network Centrality
 
05:30
For the full course see: https://goo.gl/iehZHU Follow along with the course eBook: https://goo.gl/i8sfGP 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. Produced by: http://complexitylabs.io Twitter: https://goo.gl/ZXCzK7 Facebook: https://goo.gl/P7EadV LinkedIn: https://goo.gl/3v1vwF 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.
Views: 26148 Complexity Labs
Analyzing Social Networks on Twitter
 
01:01:25
Twitter is rapidly becoming a "common carrier" of social media, a wire that transmits a variety of content rather then a social network in itself. Yet, Twitter data is rich in elements that yield to Social Network Analysis techniques and can produce unique insights into information diffusion. This course will cover: Harvesting data from Twitter via search and streaming APIs Decomposing Tweets into constituent parts Fast-and-frugal content analysis of tweets Deriving and analyzing social network data found in tweets. Analyzing friends and followers About Maksim Tsvetovat: Maksim Tsvetovat is an interdisciplinary scientist, a software engineer, and a jazz musician. He has received his doctorate from Carnegie Mellon University in the field of Computation, Organizations and Society, concentrating on computational modeling of evolution of social networks, diffusion of information and attitudes, and emergence of collective intelligence. Currently, he teaches social network analysis at George Mason University. He is also a co-founder of DeepMile Networks, a startup company concentrating on mapping influence in social media. Maksim also teaches executive seminars in social network analysis, including "Social Networks for Startups" and "Understanding Social Media for Decisionmakers".
Views: 4080 O'Reilly
Social network analysis: Considerations for data collection and analysis
 
01:10:02
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
An Introduction to Social Network Analysis: Part 1
 
01:34:56
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.
Analyze Social Networks with NVivo 11 Plus
 
29:03
Discover influencers, opinion leaders and study the information flow in a network. Use sociogram visualizations to see network relationships and interactions, and use metrics to discover critical network roles like influencers, connectors and brokers. See why Social Network Analysis has emerged as a key research technique. http://www.qsrinternational.com
Views: 2359 NVivo by QSR
Social Media Analytics Tutorial: Techniques For Social Media Analytics And Optimization
 
01:28:36
My website: http://smediahub.com/ Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization. Lecture's slides: http://videolectures.net/site/normal_dl/tag=612868/single_leskovec_social_01.pdf Tutorial website: http://snap.stanford.edu/proj/socmedia-kdd/ Jure Leskovec: http://cs.stanford.edu/people/jure/
Views: 9955 Thomas Joslyn
Hacking into the power of social networks
 
04:22
Sources for scientific journals are provided below. New videos come out every Thursday so subscribe for more videos. Visit my Facebook page for more bite sized tips and psychology information https://www.facebook.com/BiteSizePsych Also, if you like the music behind it, you should check out the musician behind it. This is his latest project: https://www.youtube.com/watch?v=wepL1wsn1B0 Sources Happiness http://www.bmj.com/content/337/bmj.a2338 Obesity http://www.nejm.org/doi/full/10.1056/NEJMsa066082 Smoking http://www.nejm.org/doi/full/10.1056/NEJMsa0706154 Cooperation https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2851803/ Loneliness http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2792572/ Weak ties https://sociology.stanford.edu/sites/default/files/publications/the_strength_of_weak_ties_and_exch_w-gans.pdf Birds of a feather http://www.annualreviews.org/doi/abs/10.1146/annurev.soc.27.1.415
Views: 45601 Bite Size Psych
Mining Online Data Across Social Networks
 
01:04:14
Capturing Data, Modeling Patterns, Predicting Behavior. Capturing Data, Modeling Patterns, Predicting Behavior - Based on collecting more than 20 million blog posts and news media articles per day, Professor Jure Leskovec discusses how to mine such data to capture and model temporal patterns in the news over a daily time-scale --in particular, the succession of story lines that evolve and compete for attention. He discusses models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring hidden networks of information flow. Learn more: http://scpd.stanford.edu/
Views: 19929 stanfordonline
Modeling Complex Social Networks: Challenges & Opportunities for Statistical Learning & Inference
 
56:23
Center for Science of Information presents as part of our Weekly Seminar series: Assistant Professor Jennifer Neville Purdue University, Depts. of Computer Science & Statistics "Modeling Complex Social Networks: Challenges & Opportunities for Statistical Learning & Inference" Recorded Wednesday, April 27, 2011 Abstract: Recently there has been a surge of interest in methods for analyzing complex social networks: from communication networks, to friendship networks, to professional and organizational networks. The dependencies among linked entities in the networks present an opportunity to improve predictions about the properties of individuals, as birds of a feather do indeed flock together. For example, when deciding how to market a product to people in MySpace or Facebook, it may be helpful to consider whether a person's friends are likely to purchase the product. This talk will give an overview of the area, presenting a number of characteristics of social network data that differentiate it from traditional inference and learning settings, and outline the resulting opportunities for significantly improved inference and learning. We will discuss techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues, statistical challenges, and potential modeling pathologies that are unique to network data.
Views: 18312 Purdue University
Social Network Analysis
 
02:06:01
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: 3736 Microsoft Research
Sentiment Analysis of Social Media Texts Part 1
 
01:29:44
Sentiment Analysis of Social Media Texts Saif M. Mohammad and Xiaodan Zhu October 25, 2014 - Morning Tutorial notes Abstract: Automatically detecting sentiment of product reviews, blogs, tweets, and SMS messages has attracted extensive interest from both the academia and industry. It has a number of applications, including: tracking sentiment towards products, movies, politicians, etc.; improving customer relation models; detecting happiness and well-being; and improving automatic dialogue systems. In this tutorial, we will describe how you can create a state-of-the-art sentiment analysis system, with a focus on social media posts. We begin with an introduction to sentiment analysis and its various forms: term level, message level, document level, and aspect level. We will describe how sentiment analysis systems are evaluated, especially through recent SemEval shared tasks: Sentiment Analysis of Twitter (SemEval-2013 Task 2, SemEval 2014-Task 9) and Aspect Based Sentiment Analysis (SemEval-2014 Task 4). We will give an overview of the best sentiment analysis systems at this point of time, including those that are conventional statistical systems as well as those using deep learning approaches. We will describe in detail the NRC-Canada systems, which were the overall best performing systems in all three SemEval competitions listed above. These are simple lexical- and sentiment-lexicon features based systems, which are relatively easy to re-implement. We will discuss features that had the most impact (those derived from sentiment lexicons and negation handling). We will present how large tweet-specific sentiment lexicons can be automatically generated and evaluated. We will also show how negation impacts sentiment differently depending on whether the scope of the negation is positive or negative. Finally, we will flesh out limitations of current approaches and promising future directions. Instructors: Saif M. Mohammad, Researcher, National Research Council Canada Saif Mohammad is a Research Officer at the National Research Council Canada. His research interests are in Computational Linguistics, especially Lexical Semantics. He develops computational models for sentiment analysis, emotion detection, semantic distance, and lexical-semantic relations such as word-pair antonymy. Xiaodan Zhu, Researcher, National Research Council Canada Xiaodan Zhu is a Research Officer at the National Research Council Canada. His research interests are in Natural Language Processing, Spoken Language Understanding, and Machine Learning. His recent work focuses on sentiment analysis, emotion detection, speech summarization, and deep learning. The instructors, along with Svetlana Kiritchenko, developed the NRC-Canada Sentiment Analysis System, which was the top-performing system in recent SemEval shared-task competitions (SemEval-2013, Task 2, SemEval-2014 Task 9, and SemEval-2014 Task 4).
Views: 32087 emnlp acl
Social Media Analytics
 
01:28:12
VideoLectures.Net Single Lectures Series View the complete series: http://videolectures.net/single_lecture_series/ Speaker: Jure Leskovec, Computer Science Department, Stanford University License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ Online social media represent a fundamental shift of how information is being produced, transferred and consumed. The present tutorial investigates techniques for social media modeling, analytics and optimization. First we present methods for collecting large scale social media data and then discuss techniques for coping with and correcting for the effects arising from missing and incomplete data. We proceed by discussing methods for extracting and tracking information as it spreads among the users. Then we examine methods for extracting temporal patterns by which information popularity grows and fades over time. We show how to quantify and maximize the influence of media outlets on the popularity and attention given to particular piece of content, and how to build predictive models of information diffusion and adoption. As the information often spreads through implicit social and information networks we present methods for inferring networks of influence and diffusion. Last, we discuss methods for tracking the flow of sentiment through networks and emergence of polarization. 0:00 Social Media Analytics: Part 1: Information flow 1:35 Information and Networks 2:37 Social Media: Big change 3:37 Social Media: Opportunities 4:01 Social Media: Value proposition 4:28 Applications: Reputation management 5:30 Applications: Citizen response 6:37 Applications: Real-time citizen journalism 7:12 Applications: Social media marketing 7:57 Applications: Human behaviour analysis 8:34 The tutorial: Social Media 9:33 Tutorial Outline (1) 9:59 Part 1 of the Tutorial: Overview 10:35 Social Media Data: Spinn3r 12:00 Tracing Information Flow
Views: 5613 VideoLecturesChannel
Meerkat Social Network Analysis Tool
 
01:22
Meerkat is a social network analysis application under development by Dr. Osmar Zaiane and his lab. It offers facilities for automated community mining, various layout algorithms for helpful visualizations, and timeframe event analysis for dynamic networks that have been observed at multiple points in time. For more information go to: http://www.aicml.ca/node/41
Views: 4013 aicmlmedia
Introduction to Social Network Analysis
 
03:03:15
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.
Information Flow and Graph Structure in Online Social Networks
 
01:10:36
Jon Kleinberg of Cornell University presents a model that tracks the sharing and dispersion of information through social media networks.
Webinar Series | Social Network Analysis: An Innovative Tool to Maximize NIBIN Lead
 
01:14:31
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.
Views: 102 Forensic Technology
Master Data Management and Social Network Analysis
 
03:49
Defining spheres of influence and analyzing customer data through social networks.
Views: 402 Pitney Bowes
Social Network Analysis with Python (PyCon APAC 2014)
 
27:26
Speaker: David Chiu As the rise of social network, the relationship between people can now be described as network graph. Through the analysis of social network, the complex people interaction can be characterized by mathematical model. Since the social network information can now being accessed by simple API call, this talk will introduce how to use python and install related package to build up simple script to access and analyze social network. In addition, this talk will introduce how to use a powerful network visualization tool, Gephi, to build up interactive figure. From this talk, the audience can follow up each provision steps to build up a powerful analysis program to analyze one's own social network. About the speaker David Chiu, 前趨勢科技工程師, NumerInfo 營運長, TW.R Officer 一位致力於提供Data as a Service 的創業者、資料科學家,熟悉巨量資料處理,暨長時間專注使用各式Data Mining 技術做資料分析;為台灣Python 及 R 社群的忠實聽眾,喜愛參與社團交流與分享,希望能多了解如何使用Python & R 讓 資料分析更簡單上手。
Views: 7536 PyCon Taiwan
Network Analysis. Lecture 15. Diffusion of innovation and influence maximization.
 
01:46:57
Diffusion of innovation. Independent cascade model. Linear threshold model. Influence maximization. Submodular functions. Finding most influential nodes in networks. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture15.pdf
Views: 3590 Leonid Zhukov
The Top Social Media Competitor Analysis Tools & How to Use Them
 
08:28
Social media is a fantastic tool to find competitors, analyze competitors, compare competitors and find out what they are doing, what’s working for them and what’s not so you can just model success instead of going through the slow process of trial and error. The other reason why it’s important to find competitors and analyze competitors is to make sure you are providing more or at least the same amount of value to your audience than they are. The first step when performing competitor research is to identify your 5 top competitors and look them up on the main social networks: Facebook, Twitter, Instagram and Linkedin. After your find competitors, collect the following information about them for each social network: Their audience size Their engagement rate Their engagement method Their posting frequency Their advantages Their disadvantages Once you’ve performed your competitor research and have collected enough information to compare competitors, you will have a clearer picture about how to improve your social media communication strategy. Then, to monitor your progress against your competitor’s progress and make sure you remain ahead, use social media competitor analysis tools depending on which platform you’re using. For example, should you have a Facebook page for your business and so do your competitors, we recommend using the built-in Facebook competitor analysis tool called “pages to watch”. This feature allows you to add any page you’d like to monitor against yours and easily compare competitors against you. The great thing about this competitor research tool is that your competitors won’t be notified that you’re monitoring them. Plus, this competitor research tool also suggests similar pages to watch. On Twitter, a tool to analyze competitors we recommend is Klout. Klout is a tool that gives you a score from 1 to 100 which rates a Twitter account’s social media influence. Most businesses have an average score of 40, however, influential accounts will have a score of 50 and above. Ideally, you’d like to have a higher Klout score than any of your competitors. Another social network analysis tool that is useful to analyze competitors is Twitter lists. Twitter lists are the equivalent of folders to categorize and manage Twitter accounts. Users can either create their own lists and make the lists “private” or “public’. Should you be using Twitter lists to monitor your competitors, make sure it is private so only you have the ability to view the list. This will allow you to check the activity of your competitors all in one place, see what they’re up to, what they’ve been tweeting, etc... and as a result improve your own social media communication strategy. When it comes to monitoring your competitors on Instagram, we recommend the social network analysis tool Talkwalker. Once you’ve collected all the information necessary that you need in order to have a clearer picture of where you stand compared to your competitors, don’t let that information go to waste. Instead, fine tune your campaigns accordingly. So you can remain one step ahead. The key when monitoring your competition and using social network analysis tools is not collecting too much information or else it’s easy to feel overwhelmed. Always improve your social media marketing efforts one step at a time.
Social Network Analysis SNA_Part_1
 
06:57
This is an analysis of informtion flow within a hypothetical company. For more information regarding the context of this screencast please visit BenjaminGentry.com
Views: 16011 btrain1525
A Quick Look at Social Network Analysis
 
01:55
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?
Network Analysis. Lecture 14.  Social contagion and spread of information.
 
01:33:17
Information diffusion. Rumor spreading models. Homogenous and mean field models. Examples. Cascades and information propagation trees. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture14.pdf
Views: 1669 Leonid Zhukov
Integration of Social Network Analysis (SNA) and Spatial Analysis (GIS)
 
54:26
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 network analysis - Introduction to structural thinking: Dr Bernie Hogan, University of Oxford
 
02:23:16
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
Johannes Wachs - Analyzing Networks In Python
 
24:50
Networks encode complex information on all kinds of interactions. We look at how a network perspective can reveal valuable information about corruption in public procurement, internal collaboration at a multinational firm, and the tone of campaigns on Twitter, all with the phenomenal NetworkX library. NetworkX is a highly productive and actively maintained library that interacts well with other libraries and environments. It inherits many Python strengths like fast prototyping and ease of teaching. We also discuss alternatives like graph-tool and igraph.
Views: 14595 PyCon SK
Social Network Analysis with the Information Workbench
 
02:12
A small showcase of how one can easily integrate Twitter data into the Information Workbench and do exploration and analysis ontop of it.
Views: 172 phaasenase
How social networks make us smarter | Alex 'Sandy' Pentland | TEDxBeaconStreet
 
11:24
By harnessing the power of our collective intelligence, can humans as a species work together to implement thoughtful solutions in an age of connectivity? In a world riddled with big problems, leading social scientist Alex 'Sandy' Pentland has heartening news. His research is discovering the power and pitfalls of social sharing on our decision-making. Pentland's field, "Social Physics" is a new way of understanding human behavior based on analysis of Big Data. By leveraging huge amounts of available consumer information and tracking idea flow, research can now make better predictions about human behavior and learn how to make minor shifts to generate massive change. Alex `Sandy' Pentland directs MIT's Human Dynamics Laboratory and the MIT Media Lab Entrepreneurship Program, co-leads the World Economic Forum Big Data and Personal Data initiatives, and is a founding member of the Advisory Boards for Nissan, Motorola Mobility, Telefonica, and a variety of start-up firms. In 2012 Forbes named Sandy one of the 'seven most powerful data scientists in the world'. In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)
Views: 29235 TEDx Talks
Improving Fraud Detection Techniques Using Social Network Analytics
 
03:03
Bart Baesens and Véronique Van Vlasselaer of KU Leuven talk to Inside Analytics host Maggie Miller about using social network algorithms to stay ahead of fraudsters. For more information about the Analytics 2014 conference, visit http://www.sas.com/events/analytics/europe/
Views: 2553 SAS Software
Using (Excel) .NetMap for Social Network Analysis
 
01:43:29
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: 16607 Harvard Ash Center
Betweenness Centrality
 
11:43
Big Data Analytics For more: http://www.anuradhabhatia.com
Views: 27189 Anuradha Bhatia
Social Network Analysis
 
02:17
Web Detective - Social Network Analysis http://www.webdetectivelaunch.com/special
Views: 84 AttentionSoft
2012-04-11 - : K-Anonymity in Social Networks: A Clustering Approach - CERIAS Security Seminar
 
53:15
Recorded: 04/11/2012 CERIAS Security Seminar at Purdue University : K-Anonymity in Social Networks: A Clustering Approach Traian Truta, Northern Kentucky University The proliferation of social networks, where individuals share private information, has caused, in the last few years, a growth in the volume of sensitive data being stored in these networks. As users subscribe to more services and connect more with their friends, families, and colleagues, the desire to use this information from the networks has increased. Online social interaction has become very popular around the globe and most sociologists agree that this will not fade away. Social network sites gather confidential information from their users (for instance, the social network site PacientsLikeMe collects confidential health information) and, as a result, social network data has begun to be analyzed from a different, specific privacy perspective. Since the individual entities in social networks, besides the attribute values that characterize them, also have relationships with other entities, the risk of disclosure increases. In this talk we present a greedy algorithm for anonymizing a social network and a measure that quantifies the information loss in the anonymization process due to edge generalization. Traian Marius Truta is an associate professor of Computer Science at Northern Kentucky University. He received his Ph.D. in computer science from Wayne State University in 2004. His major areas of expertise are data privacy and anonymity, privacy in statistical databases, and data management. He has served on the program committee of various conferences such as International Conference on Database and Expert Systems Applications (DEXA), Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), ACM Symposium of Applied Computing (SAC), and International Symposium on Data, Privacy, and E-Commerce (ISDPE). He received the Yahoo Research! Best Paper Award for Privacy, Security, and Trust in KDD 2008 (PinKDD) for the paper �A Clustering Approach for Data and Structural Anonymity in Social Networks� in 2008. For more information, including the list of research publications please see: http://www.nku.edu/~trutat1/research.html. (Visit: www.cerias.purude.edu)
Views: 3027 ceriaspurdue
The Social Network — Sorkin, Structure, and Collaboration
 
13:24
Support this channel at: http://patreon.com/LFTScreenplay Like LFTS on Facebook: https://www.facebook.com/lessonsfromthescreenplay/ Follow me at: http://twitter.com/michaeltuckerla Sorkin's dialogue is famous for being rapid-fire and full of wit. In this video I look at the function of this style, how he uses non-linear structure to frame the story, and the critical role that collaboration played in the creation of The Social Network. The Social Network Screenplay by Aaron Sorkin Directed by David Fincher Starring Jesse Eisenberg, Andrew Garfield, Justin Timberlake, Armie Hammer, Rooney mara. DP/30 Sorkin Interview: https://www.youtube.com/watch?v=Ya3jOt9K1Qk Watch the behind-the-scenes documentary: http://www.imdb.com/video/imdb/vi1594268185/ Translate this video into your language: http://www.youtube.com/timedtext_video?ref=share&v=8IAGH6k17nw Thanks to Diego Rojas for composing original music for this video! Check him out: https://soundcloud.com/diegorojasguitar Marxist Arrow by Twin Musicom is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Artist: http://www.twinmusicom.org/
Social Network Analysis: Applications & Challenges at Summer School on Social Computing.
 
01:20:42
Recommend friends / WTF Analyze and improve information / communication flow Identify influencers Finding the hidden connections Shadow profiles Optimize the structure and capacity of telephone / mobile networks Degree, Betweenness, Closeness, Eigenvector
Mathematica Experts Live: Social Network Analysis
 
16:44
In this video from Mathematica Experts Live: New in Mathematica 9, Charles Pooh shares several examples that highlight Mathematica's full suite of social network analysis functionality, including built-in access to social media data, as well as other graphs and networks enhancements. For more information about Mathematica, please visit: http://www.wolfram.com/mathematica
Views: 2816 Wolfram
EARL 2015 - Social Network Analysis in R - Amar Dhand
 
21:19
Dr. Amar Dhand, from Washington University in St. Louis on Social network analysis in R applied to stroke patients' health behaviours at EARL 2015 London - Effective Applications of the R Language For more information see: http://earlconf.com Or, on twitter, follow: http://twitter.com/earlconf
Views: 3057 Mango Solutions
Sentiment and Social Network Analysis — Laura Drummer,  Novetta Solutions
 
40:51
Traditional social network analysis is performed on a series of nodes and edges, generally gleaned from metadata about interactions between several actors. In the intelligence and law enforcement communities, this metadata can frequently be paired with data and communications content. Our analytic, SocialBee, takes advantage of this widely untapped data source to not only perform more in-depth social network analysis based on actor behavior, but also enrich the social network analysis with topic modelling, sentiment analysis, and trending over time. Through extraction and analysis of topic-enriched links, SocialBee has also been able to successfully predict hidden relationships, i.e., relationships not seen in the original dataset, but that exist in an external dataset via different means of communication. The clustering of communities based on behavior over time can be done by looking purely at metadata, but SocialBee also analyzes the content of communications which will allows for a richer analysis of the tone, topic, and sentiment of each interaction. Traditional topic modelling is usually done using natural language processing to build clusters of similar words and phrases. By incorporating these topics into a communications network stored in neo4j, we are able to ask much more meaningful questions about the nature of individuals, relationships, and entire communities. Using its topic modelling features, SocialBee can identify behavior based communities within this networks. These communities are based on relationships where a significant percentage of the communications are about a specific topic. In these smaller networks, it is much easier to identify influential nodes for a specific topic, and find disconnected nodes in a community. This talk explores the schema designed to store this data in neo4j, which is loosely based on the concept of the 'Author-Recipient-Topic' model as well as several advanced queries exploring the nature of relationships, characterizing sub-graphs, and exploring the words that make up the topics themselves. Speaker: Laura Drummer Location: GraphConnect NYC 2017
Views: 421 Neo4j

buzzfeed funny dating profiles
online dating bg
over 60 dating new zealand
cbs 3 dating show full episodes
blue eyes dating site