Networks are everywhere, but the tools for end users to access, analyze, visualize and share insights into connected structures have been absent. NodeXL, the network overview discovery and exploration add-in for Excel makes network analysis as easy as making a pie chart.
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.
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
Networks are a powerful way to understand social media.
This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.
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.
2015 #MMeasure-Marc Smith-NodeXL Mapping social media using social network ma...Marc Smith
Networks are a powerful way to understand social media.
This talk reviews the ways the NodeXL application can be used to reveal the social media networks structures around topics.
How to use social media network analysis for amplificationMarc Smith
How can social media network analysis help you get your message out? Use network maps of social media to identify the most influential contributors based on their location within the network. Use content analysis to identify the topics, hashtags and URLs of greatest interest to the "mayors" of the hashtags that matter to you.
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
Marc Smith - Charting Collections of Connections in Social Media: Creating Ma...Saratoga
An advocate for open tools, open data and open scholarships, Marc Smith strives for access to information to be available to all. Pioneering the possibilities through charting collections and creating maps with NodeXL.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
Marc Smith's presentation to the Personal Democracy Forum 2010 in New York City on June 4th, 2010 about the use of NodeXL, a social media network analysis tool, to map political topics in services like Twitter.
NodeXL is available from http://nodexl.codeplex.com
20110128 connected action-node xl-sea of connectionsMarc Smith
Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County (CFMCO.org)
An overview of the Network Overview Discovery and Exploration add-in for Excel 2007 (NodeXL), a social network analysis add-in for the familiar spreadsheet application. Visualize twitter, flickr, facebook, and email networks with just a few mouse clicks.
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
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
Using freely available tools, I've analyzed my Facebook friends to explore the networks that exist among them. The same idea could be used for identifying potential sources online.
Andy Carvin talks about his work at NPR, The Tow Center and First Look MediaAndy Carvin
Andy Carvin talks about how he used social media at NPR to cover the Arab Spring, his research at Columbia University's Tow Center for Digital Journalism, and his recent move to First Look Media.
How to use social media network analysis for amplificationMarc Smith
How can social media network analysis help you get your message out? Use network maps of social media to identify the most influential contributors based on their location within the network. Use content analysis to identify the topics, hashtags and URLs of greatest interest to the "mayors" of the hashtags that matter to you.
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
Marc Smith - Charting Collections of Connections in Social Media: Creating Ma...Saratoga
An advocate for open tools, open data and open scholarships, Marc Smith strives for access to information to be available to all. Pioneering the possibilities through charting collections and creating maps with NodeXL.
Slides from talks presented at Mammoth BI in Cape Town on 17 November 2014.
Visit www.mammothbi.co.za for details on the event. Follow @MammothBI on twitter.
2010 june - personal democracy forum - marc smith - mapping political socia...Marc Smith
Marc Smith's presentation to the Personal Democracy Forum 2010 in New York City on June 4th, 2010 about the use of NodeXL, a social media network analysis tool, to map political topics in services like Twitter.
NodeXL is available from http://nodexl.codeplex.com
20110128 connected action-node xl-sea of connectionsMarc Smith
Slides for the 28 January 2011 Presentation of "Finding direction in a sea of connection" at Hartnell College in Salinas, California, sponsored by the Community Foundation for Monterey County (CFMCO.org)
An overview of the Network Overview Discovery and Exploration add-in for Excel 2007 (NodeXL), a social network analysis add-in for the familiar spreadsheet application. Visualize twitter, flickr, facebook, and email networks with just a few mouse clicks.
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
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
Using freely available tools, I've analyzed my Facebook friends to explore the networks that exist among them. The same idea could be used for identifying potential sources online.
Andy Carvin talks about his work at NPR, The Tow Center and First Look MediaAndy Carvin
Andy Carvin talks about how he used social media at NPR to cover the Arab Spring, his research at Columbia University's Tow Center for Digital Journalism, and his recent move to First Look Media.
This presentation gives simple but effective techniques for mapping a business process. Process Mapping is a strong initial step in continuous improvement of any business process.
Charting collections of connections in social media, presented by Marc SmithSocialMedia.org
In his Brands-Only Summit Pre-Conference presentation, Social Media Research Foundation's Marc Smith explains how you can analyze your social media network to reveal key people, topics, and sub-communities.
He shares a review of his NodeXL tool and presents us with images of Twitter, Flickr, YouTube, Facebook, and email networks.
This is a large mixed deck with three main topic areas - what and why to use online interaction, some tool issues and facilitation. Very brief and not annotated, so may not be useful as a stand alone. Used at CIAT, Cali Colombia August 14 2007
Slides from my presentation at the European Foundation for Quality in Elearning about how we create connections (thus the Velcro TM) for learning anytime, anywhere.
This is the presentation Debra Askanese and Talia Klein gave at the Kishor Conference for Haredi Business Professionals.
To the best of our knowledge, all pictures used are Creative Commons and have been attributed.
Please contact us on Twitter if you have any questions or would like a copy of the presentation.
Debra: @askdebra
Talia: @TalTalK
Overview of Social Media: Trends, Stats, and What It's All AboutDebra Askanase
Putting social media history, trends, and usage in perspective for businesses getting started. This overview of social media also includes ways that businesses are using social media to succeed.
This was developed jointly by Talia Klein @TalTalk of Sparkeo.com, and Debra Askanase @asdebra of Community Organizer 2.0.
To the best of our knowledge, all pictures used are Creative Commons and have been attributed.
Please contact us on Twitter if you have any questions or would like a copy of the presentation.
Digital Habitats : stewarding technology for communities - South Africa, May ...Nancy Wright White
The general set of slides I'm using in my Technology Stewardship workshops in S. Africa, May 2010 (CSIR/Pretoria, University of Cape Town and IST in Durban)
This workshop was part of the Social Media Tract for Coalitions at CADCA's Mid Year Training Institute, July 2011. For more information on CADCA go to http://www.cadca.org and for more on the beginning discussion about the workshop see http://technologyinprevention.blogspot.com/2011/07/power-of-presence.html
Slides from the talk I presented March 17th at the IOC Online Conference http://www.internationalonlineconference.org/2010/program - I made a few post-talk adjustments to include some of the interactions and screen shots of the work of Dan Porter who provided live, electronic graphic recording of the talk.
This presentation was part of a bespoke staff development seminar at Canterbury Christ Church University on the 20th February 2014. The presentation explores how engaging with social media should be a critical skill for the 21st century researcher in building and maintaining their networks both in and beyond the University. Specifically, I wanted academic staff and postgraduate students to consider how these critical skills could be used to support, sustain and maintain academic practice within a University in the 21st century.
Developing a social media plan for your non-profit org. Consider the user and the platform. Presented to Impact100 in Baldwin County AL and at ALLA2011.
Ideas for Social Media Strategy for Southern Rural Development CenterAnne Adrian
This presentation was adapted from the National eXtension Conference http://www.slideshare.net/aafromaa/introducing-ideas-for-social-media-strategy
Please read the notes. More ideas, concepts, and references are given in the notes.
Similar to Think Link: Network Insights with No Programming Skills (20)
A network is a collection of connections. Learn how to express an edge in NodeXL. If you can make a pie chart, you can now make a network chart in NodeXL,
Course description for Optimice class on organizational network analysis (ONA) - the application of social network analysis (SNA) to business organizations.
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)
20110830 Introducing the Social Media Research FoundationMarc Smith
Description of the activities and goals of the Social Media Research Foundation. The foundation is dedicated to Open Tools, Open Data, and Open Scholarship. See: http://www.smrfoundation.org.
One project from the Social Media Research Foundation is NodeXL, the network overview, discovery and exploration add-in for Excel 2007/2010. See: http://www.smrfoundation.org
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
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Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
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Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
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Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
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Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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Think Link: Network Insights with No Programming Skills
1. A project from the Social Media Research Foundation: http://www.smrfoundation.org
2. About Me
Introductions
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
http://www.smrfoundation.org
4. Social Media Research Foundation
People Disciplines Institutions
University
Faculty
Computer Science University of Maryland
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of
Technology
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics
5. What we are trying to do:
Open Tools, Open Data, Open Scholarship
• Build the “Firefox of GraphML” – open tools for
collecting and visualizing social media data
• Connect users to network analysis – make
network charts as easy as making a pie chart
• Connect researchers to social media data sources
• Archive: Be the “Allen Very Large Telescope Array”
for Social Media data – coordinate and aggregate
the results of many user’s data collection and
analysis
• Create open access research papers & findings
• Make “collections of connections” easy for users
to manage
8. What we have done: Open Tools
• NodeXL
• Data providers (“spigots”)
– ThreadMill Message Board
– Exchange Enterprise Email
– Voson Hyperlink
– SharePoint
– Facebook
– Twitter
– YouTube
– Flickr
9. What we have done: Open Data
• NodeXLGraphGallery.org
– User generated collection
of network graphs,
datasets and annotations
– Collective repository for
the research community
– Published collections of
data from a range of social
media data sources to help
students and researchers
connect with data of
interest and relevance
21. Vertex1 Vertex 2 “Edge”
Attribute
“Vertex1”
Attribute
“Vertex2”
Attribute
@UserName1 @UserName2 value value value
A network is born whenever two GUIDs are joined.
Username Attributes
@UserName1 Value, value
Username Attributes
@UserName2 Value, value
A B
24. Social
Networks
• History:
from the
dawn of
time!
• Theory and
method:
1934 ->
• Jacob L.
Moreno
• http://en.wik
ipedia.org/wi
ki/Jacob_L._
Moreno
Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football
team.
Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease
Publishing Company.
25. A nearly social network diagram of relationships among workers in a factory
illustrates the positions different workers occupy within the workgroup.
Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and
the worker. Cambridge, UK: Cambridge University Press.
52. Welser, Howard T., Eric Gleave, Danyel
Fisher, and Marc Smith. 2007. Visualizing the
Signatures of Social Roles in Online Discussion
Groups.
The Journal of Social Structure. 8(2).
Experts and “Answer People”
Discussion starters, Topic setters
Discussion people, Topic setters
53. NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can
illustrate the ways different
locations have different values
for centrality and degree
64. SNA questions for social media:
1. What does my topic network look like?
2. What does the topic I aspire to be look like?
3. What is the difference between #1 and #2?
4. How does my map change as I intervene?
What does #YourHashtag look like?
66. strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27
Top 10 Vertices, Ranked by
Betweenness Centrality:
@strataconf
@peteskomoroch
@acroll
@oreillymedia
@orthonormalruss
@ayirpelle
@bigdata
@furrier
@marketpowerplus
@sassoftware
67. datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTC
Top 10 Vertices, Ranked by
Betweenness Centrality:
@bigpupazzoverde
@randal_olson
@twitterdata
@7of13
@yochum
@edwardtufte
@twittersports
@grandjeanmartin
@smfrogers
@albertocairo
71. [Divided]Polarize
d Crowds
[Unified]Tig
ht Crowd
[Fragmented]
Brand Clusters
[Clustered]
Communities
[In-Hub &
Spoke]Broadcast
Network
[Out-Hub &
Spoke]Support
Network
[Low probability]
Find bridge users.
Encourage shared
material.
[Low probability]
Get message out to
disconnected
communities.
[Possible transition]
Draw in new
participants.
[Possible transition]
Regularly create
content.
[Possible transition]
Reply to multiple
users.
[Undesirable
transition]
Remove bridges,
highlight divisions.
[Low probability]
Get message out to
disconnected
communities.
[High probability]
Draw in new
participants.
[Possible transition]
Regularly create
content.
[Possible transition]
Reply to multiple
users.
[Undesirable
transition]
Increase density of
connections in two
groups.
[Low probability]
Dramatically increase
density of
connections.
[High probability]
Increase
retention, build
connections.
[Possible transition]
Regularly create
content.
[Possible transition]
Reply to multiple
users.
[Undesirable
transition]
Increase density of
connections in two
groups.
[Low probability]
Dramatically increase
density of
connections.
[Undesirable
transition]
Increase population,
reduce connections.
[Possible transition]
Regularly create
content.
[Possible transition]
Reply to multiple
users.
[Undesirable
transition]
Increase density of
connections in two
groups.
[Low probability]
Dramatically increase
density of
connections.
[Low probability]
Get message out to
disconnected
communities.
[Possible transition]
Increase retention,
build connections.
[High probability]
Increase reply
rate, reply to multiple
users.
[Undesirable
transition]
Increase density of
connections in two
groups.
[Low probability]
Dramatically increase
density of
connections.
[Possible transition]
Get message out to
disconnected
communities.
[High probability]
Increase retention,
build connections.
[High probability]
Increase publication
of new content and
regularly create
content.
72. Request your own network map and report
http://connectedaction.net
73. • Central tenet
– Social structure emerges from
– the aggregate of relationships (ties)
– among members of a population
• Phenomena of interest
– Emergence of cliques and clusters
– from patterns of relationships
– Centrality (core), periphery (isolates),
– betweenness
• Methods
– Surveys, interviews, observations,
log file analysis, computational
analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
Source: Richards, W.
(1986). The NEGOPY
network analysis
program. Burnaby, BC:
Department of
Communication, Simon
Fraser University. pp.7-
16
Social Network Theory
http://en.wikipedia.org/wiki/Social_network
74. SNA 101
• Node
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
– Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster
• Key Metrics
– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
• Measure at the individual node or group level
– Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
average distance
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
– Peripheral – below average centrality
– Central connector – above average centrality
– Broker – above average betweenness
E
D
F
A
CB
H
G
I
C
D
E
A B D E
75. NodeXL
Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph
theory as easy as a pie chart, with integrated analysis of social media sources.
http://nodexl.codeplex.com
77. Goal: Make SNA easier
• Existing Social Network Tools are challenging
for many novice users
• Tools like Excel are widely used
• Leveraging a spreadsheet as a host for SNA
lowers barriers to network data analysis and
display
86. What is Social Network Analysis?
How is it useful for the humanities?
1. New framework for analysis
2. Data visualization allows new perspectives – less linear, more comprehensive
Social Network Analysis and Ancient History
Diane H. Cline, Ph.D.
University of Cincinnati
89. The Content summary
spreadsheet displays the most
frequently used URLs, hashtags,
and user names within the
network as a whole and within
each calculated sub-group.
98. What we want to do:
(Build the tools to) map the social web
• Move NodeXL to the web: (Node[NOT]XL)
– Node for Google Doc Spreadsheets?
– WebGL Canvas? D3.JS? Sigma.JS
• Connect to more data sources of interest:
– RDF, MediaWikis, Gmail, NYT, Citation Networks
• Solve hard network manipulation UI problems:
– Modal transform, Time series, Automated layouts
• Grow and maintain archives of social media network data sets for
research use.
• Improve network science education:
– Workshops on social media network analysis
– Live lectures and presentations
– Videos and training materials
99. How you can help
• Sponsor a feature
• Sponsor workshops
• Sponsor a student
• Schedule training
• Sponsor the foundation
• Donate your money, code, computation, storage,
bandwidth, data or employee’s time
• Help promote the work of the Social Media
Research Foundation
100. A project from the Social Media Research Foundation: http://www.smrfoundation.org
A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
The network of connections among people who tweeted “#My2K” over the 1-day, 21-hour, 39-minute period from Sunday, 06 January 2013 at 03:30 UTC to Tuesday, 08 January 2013 at 01:09 UTC.
The graph represents a network of 268 Twitter users whose recent tweets contained "#cmgrchat OR #smchat. The network was obtained on Friday, 18 January 2013 at 15:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-day, 21-hour, 15-minute period from Monday, 14 January 2013 at 18:23 UTC to Friday, 18 January 2013 at 15:38 UTC.
The graph represents a network of 1,227 Twitter users whose recent tweets contained "lumia. The network was obtained on Saturday, 12 January 2013 at 19:52 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 5-hour, 1-minute period from Saturday, 12 January 2013 at 14:36 UTC to Saturday, 12 January 2013 at 19:37 UTC.
The graph represents a network of 1,260 Twitter users whose recent tweets contained "flotus". The network was obtained on Friday, 18 January 2013 at 18:26 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 3-hour, 3-minute period from Friday, 18 January 2013 at 15:16 UTC to Friday, 18 January 2013 at 18:20 UTC.
The graph represents a network of 399 Twitter users whose recent tweets contained "http://www.nytimes.com/2013/01/11/opinion/krugman-coins-against-crazies.html. The network was obtained on Friday, 11 January 2013 at 14:27 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 12-hour, 32-minute period from Friday, 11 January 2013 at 01:52 UTC to Friday, 11 January 2013 at 14:24 UTC.
The graph represents a network of 388 Twitter users whose recent tweets contained "delllistens OR dellcares”. The network was obtained on Tuesday, 19 February 2013 at 17:44 UTC. There is an edge for each follows relationship. There is an edge for each "replies-to" relationship in a tweet. There is an edge for each "mentions" relationship in a tweet. There is a self-loop edge for each tweet that is not a "replies-to" or "mentions". The tweets were made over the 6-day, 21-hour, 58-minute period from Tuesday, 12 February 2013 at 19:34 UTC to Tuesday, 19 February 2013 at 17:33 UTC.
https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16540strataconf Twitter NodeXL SNA Map and Report for 2014-02-11 12-53-27The graph represents a network of 1,685 Twitter users whose recent tweets contained "strataconf", tweeted over the 8-day, 0-hour, 44-minute period from Monday, 03 February 2014 at 19:55 UTC to Tuesday, 11 February 2014 at 20:39 UTC.Top Hashtags in Tweet in Entire Graph:#Strataconf, #bigdata, #hds, #BigDataSV, #hadoop, #ddbd
https://www.nodexlgraphgallery.org/Pages/Graph.aspx?graphID=16541datavis Twitter NodeXL SNA Map and Report for Tuesday, 11 February 2014 at 18:55 UTCThe graph represents a network of Twitter users whose tweets in the requested date range contained "dataviz OR datavis“ over the 41-day, 4-hour, 5-minute period from Wednesday, 01 January 2014 at 00:01 UTC to Tuesday, 11 February 2014 at 04:06 UTCTop Hashtags in Tweet in Entire Graph:#dataviz, #bigdata,#analytics,#map,#Europe, #Datavis,#Audit,#Logs
http://portal.sliderocket.com/ATWBE/Using-SNA-to-find-and-manage-RICsC. Scott Dempwolf, PhDResearch Assistant Professor & DirectorUMD - Morgan State Center for Economic Developmenthttp://www.terpconnect.umd.edu/~dempy/Insights: many clusters are based around a county and local enterprises. E.g., the middle-left cluster is Pittsburgh metro area, with large orange Westinghouse Electric. The Philadelphia cluster in the top-right is highly connected to the bottom left, which are adjacent counties. An exception to location grouping is the top-left pharma and medical cluster, composed of several companies, universities, HHS, and an interesting arrangement of inventors in several connected fans.https://plus.google.com/photos/116499393494903612852/albums/5659635437858992593/5659734868308985794?banner=pwa&pid=5659734868308985794&oid=116499393494903612852
Prof. Diane Clinehttp://www.academia.edu/2153390/The_Social_network_of_Alexander_the_Great_Social_Network_Analysis_in_Ancient_HistoryIt’s about who you know, and who those people know, and how everyone knows each other.Data visualization tool – to see data differently.