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: 41175 Systems Academy
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: 3055 Microsoft Research
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: 27344 Mod•U: Powerful Concepts in Social Science
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
EEL 6935 Network Science and Application - Social Network Analysis and Its Application on Digital Marketing and Decision Making • Analyzed social network and the potential influence that the social network may impact on decision making • Detected the sources of complex social network by applying Susceptible In (SI) model using network centrality • Promoted the designed paradigm for a new promising digital marketing approach to efficiently target the right group
Views: 428 Bowen Zhang
Day 5, Session 3
Views: 107 CSAChannel IISc
Dr. Kayo Fujimoto specializes in Social Network Analysis (SNA) and statistical methods applied to various fields in health behavior and prevention research, including adolescent substance use, HIV/STD risk transmission, and health disparities. She aims at developing and making accessible network models to non-specialist public health researchers and practitioners to maximize their utility and impact on current public health challenges. Dr. Fujimoto engages in more interdisciplinary research in combination with biological/molecular techniques, and engage in more practical sides to promote health by translating network findings into designing, implementing, and evaluating biomedical and behavioral interventions in the area of HIV/AIDS research for high-risk populations. In her talk, Dr. Fujimoto will present introduction of social network analysis including basic concepts and methods, and how social network analysis has been used in the HIV/AIDS research and introduce current NIH funded projects. This talk is part of the Methods Seminar series put on by the Methods Core of UCLA's Center of HIV Identification, Prevention, and Treatment Services (CHIPTS)
Views: 113 uclachipts
http://paddytherabbit.com/example-facebook-friends-analysis/ I am using the Louvain method method for community detection
Views: 5946 David Sherlock
This video takes on the dreaded social network analysis. You may not use it in your assignment - but it might be worth your using in future life? See http://socnetv.sourceforge.net
Views: 7053 Micheal Axelsen
The closing plenary for ASNAC 2016. A panel discussion on the applications of Social Network Analysis. The session was chaired by Associate Professor Dean Lusher (Swinburne University). The panel members included: Jenny Lewis (The University of Melbourne), Lucia Falzon (Defence Science & Technology Group and The University of Melbourne), Galina Daraganova (Australian Institute of Family Studies) and Michael Gilding (Swinburne University of Technology)
Views: 55 Centre for Transformative Innovation
This video, shot for students at the end of the undergraduate social networks course at the University of Maine at Augusta, considers the relevance of abstract social network analytic skills in the context of a career. As you can see from this brief discussion, the ability to chart relations in a systematic manner has application to work in a wide variety of occupational spheres. The ability to conduct a social network analysis is an attractive asset for students moving on to the job market.
Views: 1637 James Cook
This workshop provides a broad overview of Social Network Analysis. In the first part of the workshop, a concise overview of theoretical concepts is provided, together with examples of data collection methods. The second section discusses network data analysis - network measurements (i.e. density, reciprocity, etc.) and node level measurements (i.e. degree centrality, betweenness centrality, etc.). The last part of the workshop introduces participants to UCINET and NetDraw, software packages used for data management, analysis and visualization.
Views: 371 Social Sciences Research Laboratories (SSRL)
See the full course: https://systemsacademy.io/courses/network-theory/ Twitter: http://bit.ly/2HobMld In this lecture, we start to lay down some of our basic language for talking about networks that comes to us from graph theory a relatively new area of mathematics that studies the properties of graphs. Transcription: When we hear the word network all sorts of things spring to mind like social networks and the Internet in particular, but the power of network theory is really in its high degree of abstraction, so the first thing for us to do is to try and start back at the beginning by forgetting what we think we know about networks and embrace the abstract language of networks what is called graph theory. In the formal language of mathematics a network is called a graph and graph theory is the area of mathematics that studies these objects called graphs. The first theory of graphs goes back to 1736, the first textbook came about in 1958 but most of the work within this field is less than a few decades old. In its essence a graph is really very simple, it consist of just two parts what are called vertices and edges. Firstly Vertices; a vertex or node is a thing, that is to say it is an entity and we can ascribe some value to it, so a person is an example of a node as is a car, planet, farm, city or molecule. All of these things have static properties that can be quantifies, such as the color of our car, the size of our farm, or the weight of our molecule. Within network science vertices are more often called nodes so we will be typically using this term during the course. Edges can be define as a relation of some sort between two or more nodes, this connection may be tangible as in the cables between computers on a network or the roads between cities within a national transportation system or these edges may by be intangible, such as social relations of friendship. Edges may be also called links, ties or relations and we will be often using this latter term during the course. The nodes belonging to an edge are called the ends, endpoints, or end vertices of the edge. Within graph theory networks are called graphs and a graph is define as a set of edges and a set vertices. A simple graph does not contain loops or multiple edges, but a multigraph is a graph with multiple edges between nodes. So where as a simple graph of a transpiration system would just tell us if there is a connection between two cities, a multigraph would show all the different connections between the two cities. Graphs can be directed or undirected. With an undirected graph edges have no orientation, for example a diplomatic relation between two nations may be mutual and thus have no direction to the edge between the nodes. These undirected graphs have unordered pairs of nodes, that means we can just switch them round, if Jane and Paul are married, we can say Jane is married to Paul or we can say Paul is married to Jane it makes no difference and thus it is an unordered pair. Twitter: http://bit.ly/2TTjlDH Facebook: http://bit.ly/2TXgrOo LinkedIn: http://bit.ly/2TPqogN
Views: 52896 Systems Academy
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: 150 Justice Research and Statistics Association
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: 285 Social Sciences Research Laboratories (SSRL)
Awesome to have you here ❤️ TIME TO CODE 🖥️ 🎧 ——————————————————————————————————————————————— JOIN THE DEVELOPER COMMUNITY 👬: ——————————————————————————————————————————————— 📺 SUBSCRIBE ON YOUTUBE: https://goo.gl/qkgzWg 👥 JOIN US ON SLACK: https://goo.gl/dbpgZR 💌 JOIN MY EXCLUSIVE MAILING LIST: https://goo.gl/qz1xeZ ——————————————————————————————————————————————— HOW TO ASK ME QUESTIONS 🎤: ——————————————————————————————————————————————— 👬 1:1 PRIVATE MENTORSHIP: https://goo.gl/P3PgC2 🎨 DM ME ON INSTAGRAM: https://www.instagram.com/jgordonfisher 👥 ASK ME ON SLACK: https://goo.gl/dbpgZR 🔗 Linkedin: https://www.linkedin.com/in/johngordonfisher/ 💬 Facebook: https://www.facebook.com/jgfishercode/ 🐤 Twitter: https://twitter.com/jgordonfisher 🖍️ Quora: https://www.quora.com/profile/John-Fisher-167 ——————————————————————————————————————————————— MORE ABOUT WHAT YOU WATCHED 🎥: ——————————————————————————————————————————————— 📜 DESCRIPTION: Learn how to build a basic social platform with the Python Flask web framework. In this video we review how to create a database, pull data in and out of that database, create a web server with python, and use python HTML templating to render a page for the user! 👨💻 CODE: https://github.com/jg-fisher/socialNetworkFeed ——————————————————————————————————————————————— BOOKS I LOVE ❤️: ——————————————————————————————————————————————— DEEP LEARNING: https://amzn.to/2LomU4y HANDS-ON MACHINE LEARNING: https://amzn.to/2JSxhIv VIOLENT PYTHON: https://amzn.to/2u02rZf BLACK-HAT PYTHON: https://amzn.to/2u02rZf CRACKING THE CODING INTERVIEW: https://amzn.to/2KNjN53
Views: 16105 John G. Fisher
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: 6683 Wolfram
In this webinar, Dr. Joanna Weill (then a Ph.D. Candidate) provides an overview of Social Network Analysis, a methodology for collecting and analyzing data, and a way of learning about the world that focuses on the relationships between people. This webinar explains what social networks are and what types of psychological questions can be addressed with this methodology. The presenter also discusses strategies for collecting social network data, basic types of data analyses, and useful resources. Please note that although social network analysis can be used to look at online social networks like Facebook and Twitter, this is not the focus of the webinar. This webinar was sponsored by the Society for the Psychological Study of Social Issues (SPSSI) Graduate Student Committee and the American Psychological Association of Graduate Students (APAGS).
Views: 270 SPSSI
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: 3865 Social Networks
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: 626 The Alan Turing Institute
In this short video we show you an example of how social network analysis improves organisational performance. Optimice TV is the Youtube channel owned and managed by Optimice (www.optimice.com.au). Optimice specialise in mapping social networks.
Views: 6538 OptimiceTV
Social Networks: A Basic Introduction in Four Minutes 1. Two Elements in Networks Tie: Ties represent relations between nodes Node: Nodes represent things that relate somehow to one another 2. Two Kinds of Networks Directed Networks: Networks in which the tie has direction (AKA "digraphs"). Examples: hitting, kissing, infecting, sending letters. Undirected Networks: Networks in which the tie has no direction. Examples: playing tennis with, married to, codefendant 3. Distance: "Geodesic" (Shortest Path) 4. Network Density Directed Networks: # actual ties / (n*(n-1)) Undirected Networks: # actual ties / (n*(n-1))/2 where n=# of nodes 4. Closeness Centrality: = 1/Farness Farness: Sum of Distance to all other Nodes 5. Degree centrality: how many ties touch a node? 6. Betweenness Centrality for Node X: Sum for all pairS of nodes (of the fraction of geodesics between A pair of nodes that have Node X in the middle) 7. Ego Networks Level 1.0: Ego's Ties to Alters Level 1.5: Level 1.0 Plus Alters' Ties to Other Alters Level 2.0: Level 1.5 Plus Alters' Ties to Alters' Alters 8. Induced Homophily: A tendency for ties to form to similar others because similar others are especially present in the social environment (group, community, society) Example: No wonder blues are mostly tied to blues... there are hardly any reds out there! 9. Choice Homophily: A tendency to choose to form ties with similar others even when different others are available in the social environment (group, community, society) Lots of blues and lots of reds out there, Yet each is mostly tied to its own kind! 10. Kozo Sugiyama's Network Design Principles in the abstract • Ties should be easy to follow from node to node • Ties should be far from one another • Ties should not cross or touch • Ties should be straight • Nodes that connect should be close • Similar nodes should be close • Central nodes should be in the center Sugiyama, Kozo. 2002. Graph Drawing and Applications for Software and Knowledge Engineers. Singapore: World Publishing Company Inc. That's easy to do in small, abstract networks... ... but large, real-world networks pose a challenge. 11. Looking for elaboration? Looking for explanation? Looking for application? Looking for more? Check out http://bit.ly/1M4RBEE Undergraduate Social Networks at uma.edu
Views: 6270 James Cook
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: 3122 Mango Solutions
Directed Graphs, Undirected Graphs, and Weighted Graphs along with a gist of relation depiction through edges. ------------------------------------------------------------------ GitHub Repository Link - https://github.com/mittrayash/Social-Network-Analysis-in-Python-Using-NetworkX My Portfolio - https://mittrayash.github.io/
Views: 1603 Yash Mittra
This video shows how to use SNA package to analyze social networks in R programming language. Learn the basics of R language and try data science! Ram Subramaniam Stanford
Views: 79504 Ram Subramaniam
This tutorial goes from import through the whole analysis phase for a citation network. Data can be accessed at http://www.cs.umd.edu/~golbeck/INST633o/Viz.shtml
Views: 71410 jengolbeck
Forensics Analysis of Social Networking applications and its importance to investigate cybercrimes. The tool I used to perform the forensic analysis was UFED Cellebrite physical analyzer and the information was extracted from an IPad.
Views: 1623 Luisana Figuera
An introduction to trust in social network analysis Table of Contents: 00:00 - Trust 00:22 - Defining Trust 02:36 - Why trust? 04:34 - General Definition 05:03 - Categories of Trust 06:32 - How Trust Develops 09:36 - Trust Asymmetry 10:33 - Context and Time 11:40 - Measuring Trust 11:55 - The Investment Game 13:20 - Trustworthiness 15:21 - Trust in Social Media 16:17 - Trust Inference 16:51 - Trust Inference Algorithms 17:33 - Inferring over many paths 19:14 - Applications of Trust 22:01 - Conclusions 22:28 - Conclusions
Views: 3952 jengolbeck
Dmitris Christopoulos (MODUL University, CEPS Luxembourg, Austria) provided an introduction to basic concepts and terminology of one of the rapidly developing approaches in network analysis: three-mode network analysis. In his speech he advocated how the extension of the two-mode (or bipartite) networks to a third-mode entity increases the complexity of matrix manipulations as it identifies densely connected subgroups, even when ties are restricted to between-entity relations. Starting from three decades ago, when Thomas Farao and Patrick Doreian proposed to develop the "theory of interpenetration" - that describes social network as consisting of three different entities with overlapping inclusions -, social scientists have began to use this perspective to investigate online community structures. International scientific conference ‘Networks in the Global World. Bridging Theory and Method: American, European, and Russian Studies’ took place in St. Petersburg State University on June 27-29, 2014. The primary goal of the ‘Networks in the Global World’ conference series is to bring together networks researchers from around the globe. It seeks to unite the efforts of various scientific disciplines in response to the key challenges faced by network studies today, and to exchange local research results – thus allowing an analysis of global processes. The idea of 2014-year event was to discuss the key current issues and problems of linking theoretical and methodological developments in network analysis. Find out more at http://www.ngw.spbu.ru/
Views: 125 Center for German and European Studies
Social multimedia refers to the multimedia content generated by social network users for social interactions. The increasing popularity of online social networks accumulates large amount of social network activity records, which makes the analysis of online social activities possible. The large-scale data have attracted people from both industrial and academic to mine interesting patterns from the hidden signals in the online users' activities. Researchers have successfully employed the signals extracted from social network content to finish tasks in a wide range of applications, including real world event prediction and content recommendation. From this perspective, we can view social multimedia as sensors, which provide online signals pulsing people's real word activities. One of the main features for social networks is social, where people post multimedia content intending to express their opinions and to communicate with other users. This talk will try to analyze the current research works on social multimedia analysis, particularly on visual sentiment and emotion analysis. We investigate the new approaches to analyzing online opinions towards different topics or tasks. In our research, started from the presidential election, we have identified one of the most important factors of social multimedia including sentiment analysis. We argue that as one of the most important signals from online social networks, sentiment and emotion analysis are competitive regarding monitoring and predicting online social activities. In this talk, we summarize our results on both visual sentiment and emotion analysis as well as other related topics. See more on this video at https://www.microsoft.com/en-us/research/video/sentiment-emotion-analysis-social-multimedia-methodologies-applications/
Views: 638 Microsoft Research
KXEN Social Network Analysis Demonstration - This demo shows two applications of social network analysis using KXEN InfiniteInsight Social running on top of SAP Sybase IQ. 1. The first shows the use of a direct network to analyze the potential for customer churn at a telco company based on the calling activity of a population of individuals. 2. The second is a movie recommendation application based on an indirect network of movies related by common attributes. See the power of predictive analytics running on top of SAP Sybase IQ that can churn through large amounts of data at high speeds.
Views: 12340 SAP Technology
1/28/2013 Mark Handcock - UCLA "Statistical Modeling of Social Networks" "In this talk we give an overview of social network analysis from the perspective of a statistician. The networks field is, and has been, broadly multidisciplinary with significant contributions from the social, natural and mathematical sciences. This has lead to a plethora of terminology, and network conceptualizations commensurate with the varied objectives of network analysis. As the primary focus of the social sciences has been the representation of social relations with the objective of understanding social structure, social scientists have been central to this development. We illustrate these ideas with Exponential-family random graph models (ERGM) which attempt to represent the complex dependencies in networks in a parsimonious, tractable and interpretable way. A major barrier to the application of such models has been lack of understanding of model behavior and a sound statistical theory to evaluate model fit. This problem has at least three aspects: the specification of realistic models; the algorithmic difficulties of the inferential methods; and the assessment of the degree to which the network structure produced by the models matches that of the data. We will also consider latent cluster random effects models and touch upon issues of the sampling of networks and partially-observed networks. We illustrate these methods using the "statnet" open-source software suite (http://statnet.org)."
Views: 3938 UCLABEC