You use social networks every day, but how can we understand how they work to affect our decisions, our careers, our health, and our histories? The field of Social Network Analysis is the dynamic and highly adaptable group of techniques that let us quantify and understand the complex structures and flows of relationships, thoughts, and things between people around the world. Look at your own social networks at these links: Check your own personal Facebook social network with Touchgraph: http://www.touchgraph.com/facebook Check your own personal LinkedIn social network with Socilab: http://socilab.com/ Check your own personal Twitter social network with Mentionmapp: http://mentionmapp.com/ Social Network Analysis can enrich the research of faculty and the studies of students—look for workshops run by the Duke Network Analysis Center and classes featuring graph theory, network theory, and social networks. Networks are everywhere—what will you discover with them?
Views: 27723 Mod•U: Powerful Concepts in Social Science
See the full course: https://systemsacademy.io/courses/complexity-theory/ Twitter: http://bit.ly/2HobMld A brief overview to the new area of social network analysis that applies network theory to the analysis of social relations. 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. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 41348 Systems Academy
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: 3883 Social Networks
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: 46666 Bite Size Psych
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: 3073 Microsoft Research
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
Views: 642 The Alan Turing Institute
See the full course: https://systemsacademy.io/courses/social-complexity/ Twitter: http://bit.ly/2HobMld 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?
Views: 2527 Systems Academy
See the full course: https://systemsacademy.io/courses/network-theory/ Twitter: http://bit.ly/2HobMld In this module, we talk about one of the key concepts in network theory, centrality. Centrality gives us some idea of the node's position within the overall network and it is also a measure that tells us how influential or significant a node is within a network although this concept of significance will have different meanings depending on the context. Transcription: In the previous module we talked about the degree of connectivity of a given node in a network and this leads us to the broader concept of centrality. Centrality is really a measure that tells us how influential or significant a node is within the overall network, this concept of significance will have different meanings depending on the type of network we are analyzing, so in some ways centrality indices are answers to the question "What characterizes an important node?" From this measurement of centrality we can get some idea of the nodes position within the overall network. The degree of a node’s connectivity that we previously looked at is probably the simples and most basic measure of centrality. We can measure the degree of a node by looking at the number of other nodes it is connected to vs. the total it could possibly be connected to. But this measurement of degree only really captures what is happening locally around that node it don’t really tell us where the node lies in the network, which is needed to get a proper understanding of its degree centrality and influence. This concept of centrality is quite a bit more complex than that of degree and may often depend on the context, but we will present some of the most important parameters for trying to capture the significance of any given node within a network. The significance of a node can be thought of in two ways, firstly how much of the networks recourses flow through the node and secondly how critical is the node to that flow, as in can it be replaced, so a bridge within a nations transpiration network may be very significant because it carries a very large percentage of the traffic or because it is the only bridge between two important locations. So this helps us understand significance on a conceptual level but we now need to define some concrete parameters to capture and quantify this intuition. We will present four of the most significant metric for doing this here; Firstly as we have already discussed a nodes degree of connectivity is a primary metric that defined its degree of significance within its local environment. Secondly, we have what are called closeness centrality measures that try to capture how close a node is to any other node in the network that is how quickly or easily can the node reach other nodes. Betweenness is a third metric we might use, which is trying to capture the nodes role as a connector or bridge between other groups of nodes. Lastly we have prestige measures that are trying to describe how significant you are based upon how significant the nodes you are connect to are. Again which one of these works best will be context dependent. ￼So to talk about closeness then; closeness maybe defined as the reciprocal of farness where the farness of a given node is defined as the sum of its distances to all other nodes. Thus, the more central a node is the lower its total distance to all other nodes. Closeness can be regarded as a measure of how long it will take to spread something such as information from the node of interest to all other nodes sequentially; we can understand how this correlates to the node’s significance in that it is a measurement of the nodes capacity to effect all the other elements in the network. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 28625 Systems Academy
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: 20188 stanfordonline
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: 4352 O'Reilly
The network of participants in open source projects can be mapped and visualized using social network analysis. This visualization can identify the key participants involved in various clusters that make up the project, providing a map of how individuals are collaborating to fulfill tasks. The information can be used to identify key members such as knowledge brokers, help better integrate peripheral members, and facilitate locating experts or possessors of relevant tacit knowledge. I will discuss a case study of the TikiWiki open source community comparing (1) the traditional approach of analyzing mailing list exchanges, and (2) a more interesting analysis of the wiki used by the community.
Views: 13012 koth55
Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given. Some research examples are provided. Concepts, substantive topics, and theories include social capital, Granovetter’s weak ties argument, Small World Studies, Burt’s structural holes argument, the application of SNA to collective action and social movements, amongst others.
Views: 292 Social Sciences Research Laboratories (SSRL)
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: 4159 O'Reilly
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: 6685 Wolfram
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: 3991 Leonid Zhukov
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: 2504 NVivo by QSR
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: 1843 Leonid Zhukov
Statistics and Data Series presentation by Dr. Anabel Quan-Haase, Mar. 20, 2013 at The University of Western Ontario: "Social Network Analysis: Examining Community Level Effects." The presentation built on the introduction to social network analysis presented in last year's Statistics and Data Series (see slide presentation). After a brief overview of the social network approach, she focused on analysis of the effects of community characteristics. The techniques of analysis were illustrated with examples using the procedures. Slides for this presentation are online at the RDC website. The Statistics and Data Series is a partnership between the Centre for Population, Aging and Health and the Research Data Centre. This interdisciplinary series promotes the enhancement of skills in statistical techniques and use of quantitative data for empirical and interdisciplinary research. More information at http://rdc.uwo.ca
Views: 2874 Western University
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: 2841 Wolfram
Jon Kleinberg of Cornell University presents a model that tracks the sharing and dispersion of information through social media networks.
Views: 1275 Becker Friedman Institute at UChicago - BFI
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: 7634 jengolbeck
Anatoliy Gruzd (Dalhousie University) discusses how to do automated analysis of information and social networks using social media data. As social creatures, our online lives just like our offline lives are intertwined with others within a wide variety of social networks. Each retweet on Twitter, comment on a blog or link to a Youtube video explicitly or implicitly connects one online participant to another and contributes to the formation of various information and social networks. Once discovered, these networks can provide researchers with an effective mechanism for identifying and studying collaborative processes within any online community. However, collecting information about online networks using traditional methods such as surveys can be very time consuming and expensive. The presentation explores automated ways to discover and analyze various information and social networks from social media data.
http://www.ted.com We're all embedded in vast social networks of friends, family, co-workers and more. Nicholas Christakis tracks how a wide variety of traits -- from happiness to obesity -- can spread from person to person, showing how your location in the network might impact your life in ways you don't even know. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. Featured speakers have included Al Gore on climate change, Philippe Starck on design, Jill Bolte Taylor on observing her own stroke, Nicholas Negroponte on One Laptop per Child, Jane Goodall on chimpanzees, Bill Gates on malaria and mosquitoes, Pattie Maes on the "Sixth Sense" wearable tech, and "Lost" producer JJ Abrams on the allure of mystery. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Watch a highlight reel of the Top 10 TEDTalks at http://www.ted.com/index.php/talks/top10
Views: 272218 TED
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
Views: 70 Ponnurangam Kumaraguru
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: 4062 aicmlmedia
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: 18666 Purdue University
A small showcase of how one can easily integrate Twitter data into the Information Workbench and do exploration and analysis ontop of it.
Views: 175 phaasenase
14th International Society of Scientometrics and Informetrics Conference (ISSI 2013). Part 2: Johan Bollen: "Social Network Analysis" https://phaidra.univie.ac.at/detail_object/o:300056 Produced by the University of Vienna + Information Assistant 1. Vorspann 00:00:00 2. Social media: Many to many 00:00:09 3. Facebook and Twitter. Wisdom of the crowd 00:09:04 4. Where does the collective mood come from? 00:20:30 Weitere Videos finden Sie in der Videosammlung unseres Medienarchives: http://bibliothek.univie.ac.at/zb-physik-fb-chemie/videosammlung.html
Views: 438 ZBPHVIDEO
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: 2624 SAS Software
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 â��A Clustering Approach for Data and Structural Anonymity in Social Networksâ�� 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: 3209 ceriaspurdue
Mahalia Miller, a National Science Foundation Graduate Research Fellowship Recipient, is a PhD Candidate at Stanford University in Civil Engineering and Computer Science, advised by Jack Baker. Her research focuses on quantifying the risk of extreme events to spatially-distributed networks, primarily our transportation infrastructure. To do this risk assessment, she leverages methods from Computer Science social and information network analysis, earthquake models form seismology (geophysics), and transportation models form Civil Engineering. The results will allow decision makers to reduce their vulnerability before the simulated disaster strikes.
Views: 240 Mahalia Miller
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: 17557 Harvard Ash Center
Social network analysis is catching on as a management tool in corporations who seek to understand how information and influence work within their companies. A growing number of higher education institutions are starting to turn to this model now to better understand how to communicate to their faculty and staff members. Join guest host Susanna Williams to learn more about this intriguing tool and how campuses around the country are working with it.
Views: 104 Higher Ed Live
Cristian Pasquaretta (IPHC, Strasbourg) - Social Network Analysis in Drosophila Efficiency of information transmission relies on a presence of a minimum triadic structure composed by a sender, a receiver and a transmission channel. Generally, if the sender and the receiver own a common communication code, the information detected would produce a reduction in the uncertainty of the receiver and a consequently change in its behaviour. In complex living system the minimum triadic structure needed for information transmission is usually replaced by a more complex structure where information can be obtained from several senders differently. A complex system where actors are connected each other can be easily described as a network system in which information flow among the actors following both random and, more often, non random movements. Here we introduce methods and preliminary results from a network analysis in Drosophila during information transmission trials. We automatically detected number of interactions, evolution of the contacts within the time and the utilization distribution of the arena where individuals (both senders and receivers) were recorded during experimental video tracking. We calculated and compared the most used network measures to detect their reliability in information transmission processes and we show how individual differences in exploration behaviour of the arena influence the number of interactions started and received by individuals.
Views: 287 Cédric Sueur
Tuesday, March 12, 2013 Integrating Venue-Based Social Network Analysis and Geographic Information System Analysis to Guide Targeted HIV Prevention Presented by: Ian W. Holloway, PhD, MSW, MPH Assistant Professor, Department of Social Welfare, UCLA Luskin School of Public Affairs
Views: 817 uclachipts
DOWNLOAD Lab Code & Cheat Sheet: https://drive.google.com/open?id=0B2JdxuzlHg7OYnVXS2xNRWZRODQ So you want to get started with social network analysis but need a foundation or a refresher? This video covers exactly what we mean by a “network” and is the start of a series where we give you the basic concepts and options, and we walk you through a Lab where you can experiment with designing a network on your own in R. Hosted by Jonathan Morgan and the Duke University Network Analysis Center. Further training materials available at https://dnac.ssri.duke.edu/intro-tutorials.php Duke Network Analysis Center: https://dnac.ssir.duke.edu
Views: 3720 Mod•U: Powerful Concepts in Social Science
Sueur Cédric (IPHC, Strasbourg), Introduction (Structure and Function of Social Networks). In most animal and human populations, not everybody interacts with everybody else and we see a highly structured social organization that reflects differences between individuals in the number of their social interactions, the degree to which some individuals are central or peripheral to the population network, and the tendency to interconnect different communities that form substructures within networks. More recent "evolutionary graph theory" models use networks to quantify social heterogeneity and account for it, in models of, for example, the evolution and maintenance of cooperation. Social network analyses are a powerful tool used to assess individuals' associations from the population to the group level. There is now growing support for the concept that the structure of a network is linked to its functioning, and is thus selected in order to increase the fitness of its members. Different examples in mammals or insects not only emphasize how individual characteristics (temperament, physiology, sociality, etc.) can influence a network, but also how the network influences individual behaviour via a feedback loop. Several studies showed that social networks constrain many social phenomena such as information or disease transmission. For instance, some key individuals may favour the diffusion of information or disease by their position in the network. How individuals are connected together in social species, and also how these connections may favour the information flow and enhance the decision efficiency are still unknown. We need now to better understand how the structure of social network may enhance fitness of group members and how this structure might be evolutionary selected. Septembre 2013, ouverture d'Ethobiosciences - Cabinet d'Ethologie: Expertise et Recherche en Bien-être et Comportement Animal http://ethobiosciences.com
Views: 243 Cédric Sueur
This webinar will discuss two Social Network Analysis projects that the Philadelphia Police Department undertook. The first project examined the extent of shared connections among shooting victims through network analysis; in particular, the analysis examined cross-divisional connections by combining the network analysis and GIS. The second project applied SNA to understand connections among gangs at the group level across the city. The project focused on 1) identifying the extent and nature of positive/negative connections among gangs and 2) developing a web-based application that visualizes the result of SNA on a map. Presenters: George Kikuchi, Research & Information Analyst Supervisor, Philadelphia Police George is a supervisory analyst at the Delaware Valley Intelligence Center, the Philadelphia Police Department. He oversees a team of analysts that conducts strategic crime analysis and . Matthew Lattanzio, Analyst Matthew has worked for the Philadelphia Police Department for 6 years. His background includes investigative support at the Real-Time Crime Center, a variety of quantitative crime analysis, and application development. Kevin Thomas, Director of Research and Analysis Kevin is the Director of Research and Analysis Unit where he oversees GIS, Statistics, and Analysis sections that conduct both tactical and strategic analysis. R&A also centrally warehouses a variety of data sources across the department. R&A also developed a web-based link analysis application by leveraging the centrally managed databases.
Views: 162 Justice Research and Statistics Association
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Learn how the Sentinel Visualizer software program uses Social Network Analysis (SNA) to find the most central players in any network using a variety of metrics. Find hidden relationships among people, places, things, and events. Use SNA metrics like Betweenness, Closeness, Degree, Centrality Eigenvalue, Hub, and Authority to visualize the hubs, spokes, and powerful people who span cells. For more information on SNA, visit: http://www.fmsasg.com/SocialNetworkAnalysis
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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.
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