Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
This chapter gives information about Social media analytics, Social network analysis, Text analytics, stopwords, tokenization, n-grams, Trend analytics, TF-IDF, Stemming and lemmatization
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Social Network Analysis & an Introduction to ToolsPatti Anklam
This presentation was delivered as part of an intense knowledge management curriculum. It covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
This chapter gives information about Social media analytics, Social network analysis, Text analytics, stopwords, tokenization, n-grams, Trend analytics, TF-IDF, Stemming and lemmatization
Network measures used in social network analysis Dragan Gasevic
Definition of measures (diameter, density, degree centrality, in-degree centrality, out-degree centrality, betweenness centrality, closeness centrality) used in social network analysis. The presentation is prepared by Dragan Gasevic for DALMOOC.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Gephi is an open source software for graph and network analysis. It uses a 3D render engine to display large networks in real-time and to speed up the exploration. A flexible and multi-task architecture brings new pos- sibilities to work with complex data sets and produce valuable visual results. We present several key features of Gephi in the context of interactive exploration and interpretation of networks. It provides easy and broad access to network data and allows for spatializing, fil- tering, navigating, manipulating and clustering
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Social Network Analysis & an Introduction to ToolsPatti Anklam
This presentation was delivered as part of an intense knowledge management curriculum. It covers the basics of network analysis and then goes into the different types of tool that support analyzing networks.
Social Network Analysis - Lecture 4 in Introduction to Computational Social S...Lauri Eloranta
Fourth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
Che cos'è una rete sociale, come nasce, a che cosa serve, come si trasforma in una rete creativa...
Il volume di Giuseppe RIva "I social network" pubblicato dal Mulino, Bologna.
Part 1: Concepts and Cases (the language of networks, networks in organizations, case studies and key concepts)
Part 2: (Starts on #44) Mapping Organizational, Personal, and Enterprise Networks: Tools
An update to last year's Social Network Analysis Introduction and Tools...
Ethical and Legal Issues in Computational Social Science - Lecture 7 in Intro...Lauri Eloranta
Seventh lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Social Network Analysis: applications for education researchChristian Bokhove
What is your talk about?
This seminar will illustrate various social network analysis (SNA) techniques and measures and their applications to research problems in education. These applications will be illustrated from our own research utilising a range of SNA techniques.
What are the key messages of your talk?
We will cover some of the ways in which network data can be collected and utilised with other research data to examine the relationships between network measures and other attributes of individuals and organisations, and how it can be linked to other approaches in multiple methods studies.
What are the implications for practice or research from your talk?
SNA is an approach that draws from theories of social capital to study the relational ties that exist between actors or institutions in a specific context. Such ties might include learning exchanges or advice-seeking interactions. SNA techniques allow researchers to incorporate the interdependence of participants within their research questions, whereas many traditional techniques assume our participants, and their responses to our questions, are independent of one another.
Basics of Computation and Modeling - Lecture 2 in Introduction to Computation...Lauri Eloranta
Second lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Simulation in Social Sciences - Lecture 6 in Introduction to Computational S...Lauri Eloranta
Sixth lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
Social Network Analysis (SNA) and its implications for knowledge discovery in...ACMBangalore
Social Network Analysis (SNA) and its implications for knowledge discovery in Informal Networks- Talk by Dr Jai Ganesh, SETLabs, Infosys at Search and Social Platforms tutorial, as part of Compute 2009, ACM Bangalore
An introductory-to-mid level to presentation to complex network analysis: network metrics, analysis of online social networks, approximated algorithms, memorization issues, storage.
This thesis proposes to help analyzing the characteristics of the heterogeneous social networks that emerge from the use of web-based social applications, with an original contribution that leverages Social Network Analysis with Semantic Web frameworks. Social Network Analysis (SNA) proposes graph algorithms to characterize the structure of a social network and its strategic positions. Semantic Web frameworks allow representing and exchanging knowledge across web applications with a rich typed graph model (RDF), a query language (SPARQL) and schema definition frameworks (RDFS and OWL). In this thesis, we merge both models in order to go beyond the mining of the flat link structure of social graphs by integrating a semantic processing of the network typing and the emerging knowledge of online activities. In particular we investigate how (1) to bring online social data to ontology-based representations, (2) to conduct a social network analysis that takes advantage of the rich semantics of such representations, and (3) to semantically detect and label communities of online social networks and social tagging activities.
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
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Slides for talk at ConTech 2011 the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.
Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
LSS'11: Charting Collections Of Connections In Social MediaLocal Social Summit
Keynote Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
Everything is connected: people, information, events and places. A practical way of making sense of the tangle of connections is to analyze them as networks. The objective of this workshop is to introduce the essential concepts of Social Network Analysis (SNA). It also seeks to show how SNA may help organizations unlock and mobilize these informal networks in order to achieve sustainable strategic goals. After discussing the essential concepts in theory of SNA, the computational tools for modeling and analysis of social networks will also be introduced in this presentation.
This is a presentation that describes at a high level some of the work we've been performing related to NodeXL and it's use to understand social media networks.
2014 TheNextWeb-Mapping connections with NodeXLMarc Smith
Slides from a talk at the 2014 TheNextWeb in Amsterdam.
NodeXL social media network analysis of Twitter reveals six common structures in Twitter networks.
Data Mining In Social Networks Using K-Means Clustering Algorithmnishant24894
This topic deals with K-Means Clustering Algorithm which is used to categorize the data set into clusters depending upon their similarities like common interest or organization or colleges, etc. It categorize the data into clusters on the basis of mutual friendship.
This ppt is 8 slides.
The topics covered in this ppt are Social Networking, Social Networks, Social Networks Analysis, Applications of Social Networks, Future of Social Networking, and Top 10 Social networking sites
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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7. weighted networks
well suited for representing the
intensity of relationships, the
number of interactions (e.g. mails),
or the number of affiliations (e.g.
shared links).
1
0.5
1
2
3
8. labelled networks
well suited for representing
the type of relationships
family
colleague
father
friend
follow
follow
father
9. Social network analysis
helps understanding and exploiting the key
features of social networks in order to manage
their assets, their life cycle and predict their
evolution.
10. What for?
• To control information flow
• To foster communication
• To improve network resilience
• To trust or not
17. Centrality: strategic positions
beetweenness centrality focuses
on intermediary actors
reveals brokers and privileged
actor in the information flow
[Freeman 1979]
"A place for good ideas"
[Burt 1992] [Burt 2004]
21. tendency to bind with similar others
"Birds of a feather flock together"
"interaction produces similarity, while
similarity produces interaction"
[Mika 2005]
32. Analyze your facebook network
1. Extract it first with netvizz: http://
apps.facebook.com/netvizz
2. Open it with my favorite graph
visualization tool: Gephi http://
www.gephi.org
45. "One way to begin using NodeXL is to type in your own
edge list. For example, you might type the name of
people who are friends in each row filling in the Vertex 1
and Vertex 2 columns"
46. "Click on the Show Graph button (directly above the
graph pane) to show the network of friendships"
47. "To calculate graph metrics first click on the
Graph Metric button on the Analysis section of
the NodeXL Ribbon."
48. "Vertex metrics can be mapped onto visual attributes.
The graph legend shows that Degree is mapped to Size
and Betweenness Centrality is mapped to Opacity."
49. You can now handle your
social capital
The social capital is the "resources embedded in one's
social networks, resources that can be accessed or
mobilized through ties in the networks" [Lin 2008]
http://www.kstoolkit.org/Social+Network+Analysis
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
Community detection helps understanding the global structure of a network and the distribution of actors and activities.
Moreover, the community structure influences the way information is shared and the way actors behave.
Information spread quickly in a community and is shared by most of it members.
Community detection helps understanding the global structure of a network and the distribution of actors and activities.
Moreover, the community structure influences the way information is shared and the way actors behave.
Information spread quickly in a community and is shared by most of it members.
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
The centrality highlights the most important actors of the network and three definitions have been proposed by Freeman.
The degree centrality considers nodes with the higher degrees (number of adjacent edges).
The closeness centrality is based on the average length of the paths linking a node to others and reveals the capacity of a node to be reached.
The betweenness centrality focuses on the capacity of a node to be an intermediary between any two other nodes. A network is highly dependent on actors with high betweenness centrality due to their position as intermediaries and brokers in information flow.
La théorie de l'équilibre de Heider considère que le produit des sentiments dans un réseaux social doit être positif pour que les interactions dans le réseau soient dans un état d'équilibre.
"Il semble que le terme « masse critique » vienne d'une observation du trafic routier en Chine, où sans feux de signalisation aux croisements, les cyclistes attendent d'être assez nombreux, de faire masse pour s'engager et traverser ensemble. "
Le principe de masse critique dans un réseau social correspond à un niveau d'activité à partir duquel le réseau change d'état de manière permanente. Par exemple, à partir d'un certain nombre de personne et d'un volume d'activité minimale d'interaction, un groupe de personne devient une communauté d'intérêt.
Semantic Web benefit:
Platform interoperability, common models
Structured Actors, structured resources
Different semantic link different community detection perspectives cross communities membership!!!
Semantic of interaction semantic community detection!!!
Community detection helps understanding the global structure of a network and the distribution of actors and activities.
Moreover, the community structure influences the way information is shared and the way actors behave.
Information spread quickly in a community and is shared by most of it members.