This document summarizes two user studies that evaluated NodeXL, an open-source social network analysis tool integrated with Microsoft Excel, and its effectiveness for teaching SNA concepts. 21 graduate students with varying technical backgrounds used NodeXL to analyze online communities. The studies found that NodeXL was usable for a diverse range of users and its integrated metrics and visualizations helped spark insights and facilitated understanding of SNA techniques. Lessons learned can help educators, researchers, and developers improve SNA tools.
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
Social network analysis for modeling & tuning social media websiteEdward B. Rockower
Social Network Analysis of a Professional Online Social Media Collaboration Community. Tuning and optimizing based on observed social network dynamics and user behavior.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
2009 - Node XL v.84+ - Social Media Network Visualization Tools For Excel 2007Marc Smith
Overview of the NodeXL project (Network Overview, Discovery and Exploration) that adds social network metrics and visualization features to Excel 2007. Contains updated images from version .84 of the NodeXL project.
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.
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.
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.
LAK13 Tutorial Social Network Analysis 4 Learning Analyticsgoehnert
Slides of the tutorial "Computational Methods and Tools for Social Network Analysis Networked Learning Communities" at the LAK 2013 in Leuven.
Tutorial Homepage:
http://snatutoriallak2013.ku.de/index.php/SNA_tutorial_at_LAK_2013
Conference Homepage:
http://lakconference2013.wordpress.com/
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
An Sna-Bi Based System for Evaluating Virtual Teams: A Software Development P...ijcsit
The dependence of today's collaborative projects on knowledge acquisition and information dissemination
emphasizes the importance of minimizing communication breakdowns. However, as organizations are
increasingly relying on virtual teams to deliver better and faster results, communication issues come to the
forefront of project managers' concerns. This is particularly palpable in software development projects
which are increasingly virtual and knowledge-consuming as they require continuous generation and
upgrade of shared information and knowledge. In a previous work, we proposed an SNA-BI based system
(Covirtsys) that supplements the Analytics modules of the collaborative platform in order to offer a
complementary analysis of communication flows through a network perspective. This paper concerns the
application of this system on a software development project virtual team and shows how it can bring new
insights that could help overcome communication issues among team members.
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 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.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
2009 - Node XL v.84+ - Social Media Network Visualization Tools For Excel 2007Marc Smith
Overview of the NodeXL project (Network Overview, Discovery and Exploration) that adds social network metrics and visualization features to Excel 2007. Contains updated images from version .84 of the NodeXL project.
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.
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.
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.
LAK13 Tutorial Social Network Analysis 4 Learning Analyticsgoehnert
Slides of the tutorial "Computational Methods and Tools for Social Network Analysis Networked Learning Communities" at the LAK 2013 in Leuven.
Tutorial Homepage:
http://snatutoriallak2013.ku.de/index.php/SNA_tutorial_at_LAK_2013
Conference Homepage:
http://lakconference2013.wordpress.com/
A presentation describing application of Node XL into analyzing social networks.
Made as part of project work for ITB course at VGSOM IIT Kharagpur.
By : Mayank Mohan
Anuradha Chakraborty
( Batch of 2012)
An Sna-Bi Based System for Evaluating Virtual Teams: A Software Development P...ijcsit
The dependence of today's collaborative projects on knowledge acquisition and information dissemination
emphasizes the importance of minimizing communication breakdowns. However, as organizations are
increasingly relying on virtual teams to deliver better and faster results, communication issues come to the
forefront of project managers' concerns. This is particularly palpable in software development projects
which are increasingly virtual and knowledge-consuming as they require continuous generation and
upgrade of shared information and knowledge. In a previous work, we proposed an SNA-BI based system
(Covirtsys) that supplements the Analytics modules of the collaborative platform in order to offer a
complementary analysis of communication flows through a network perspective. This paper concerns the
application of this system on a software development project virtual team and shows how it can bring new
insights that could help overcome communication issues among team members.
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 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.
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.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
The Mathematics of Social Network Analysis: Metrics for Academic Social NetworksEditor IJCATR
Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a
social network. Such networks can be casual, like those on social media sites, or formal, like academic social networks. Each of these
networks is characterised by underlying data which defines various features of the network. Keeping in view the size and diversity of
these networks it may not be possible to dissect entire network with conventional means. Social network visualization can be used to
graphically represent these networks in a concise and easy to understand manner. Social network visualization tools rely heavily on
quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics
used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that
are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also
outlined
What network simulator questions do users ask? a large-scale study of stack o...nooriasukmaningtyas
The use of network simulator as a modern tool in analyzing and predicting the behaviour of computer networks has grown to reduce the complexity of its accuracy measurement. This attracts researchers and practitioners to share problems and discuss them to improve the features. To communicate the related issues, users move to online questionanswering platforms. Although recent studies have shown the popularity and benefits of adopting network simulation tools, the challenges users face in using the network simulator remain unknown. In this research paper, we examine 2,322 network simulator related stack overflow question posts to gain insights into the topics and challenges that users have discussed. We adopt the latent dirichlet allocation model to understand the topics discussed in stack overflow. We then investigate the popularity and difficulty of each topic. The results show that users use stack overflow as an implementation guideline for the network simulation model. We determine 8 discussion topics that are merged into 5 major categories. Simulation model configuration is the most useful topic for users. We also observe that target network protocol modification and network simulator installation are the most popular topics. Network simulator installation and target network protocol modification issues have been challenging for most users. The findings also highlight future research that suggests ways to help the network simulator community in the early stages to overcome the popular and difficult topics faced when using network simulation tools.
CLOUD COMPUTING IN THE PUBLIC SECTOR: MAPPING THE KNOWLEDGE DOMAINijmpict
Cloud computing is a key element in many nations’ pursuit of fast-tracked digital transformation and the
quick implementation of digital tools but is still facing considerable barriers due to the distinct challenges
that information technology adoption faces in public sector environments. Using scientometric data from
the Web of Science database, this study explores the current state of research and the structure of the
public sector cloud computing knowledge domain in a novel way, utilizing the CiteSpace visual analytic
software to produce knowledge maps that visualize public sector cloud computing research in terms of
publication activity, constituent authors, and publication venues, as well as exploring the intellectual base
of the knowledge domain. For public sector cloud computing researchers and practitioners, the study
provides visual insights and analyses that support future research, collaboration, and evidence-based
cloud computing implementation and utilization.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Tools and Techniques for Designing, Implementing, & Evaluating Ubiquitous Com...ijceronline
Interactive systems in the mobile, ubiquitous and virtual environments are at a stage of development where designers and developers are keen to find out more about design, use, and usability of these systems. Ubiquitous Computing is the design, implementation and usability that highlight the theories, techniques, tools and best practices in these environments. This paper shows that usable and useful systems that can be achieved in ways that will improve usability to enhance user experience. Research on the usability issues for young children, teenagers, adults and the elderly is presented with different techniques for the mobile, ubiquitous and virtual environments. Interactive frameworks in the portable, omnipresent, and virtual situations are at a phase of advancement where creators and engineers are quick to discover more about the outline, use, and ease of use of these frameworks. The objective of this research paper is to assess the tools and techniques for designing, implementing, and evaluating ubiquitous computing systems used by developers so as to formulate practical solutions that address the functionality of these systems. Ideal systems ensure that designers are able to develop and predict usability of systems at all the stages of virtual environments. This is particularly essential as it increases the experience of the users. This requires one to use the best tool and techniques backed by theories to practice the same. However this varies across different fields such as ubiquitous and mobile environments. In addition all the computing tools have to share visionary tools that allow them to network while at the same time they are processing and distinctively modeling the user interface. Some of the main methods that are used for smart devices include tools such as tabs, boards and pads. Various tools are usually used in the design of the works of the computer. The need to select appropriate techniques that will allow for the efficient use of the chosen techniques for the devices is thus a necessity. This implies that the selection of such tools should be based on set out effective techniques that have been tested so that the required output is achieved.
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.
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,
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.
Think Link: Network Insights with No Programming SkillsMarc Smith
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.
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
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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:
State of global ICS asset and network exposure
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
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
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
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
2009-Social computing-First steps to netviz nirvana
1. First Steps to NetViz Nirvana: Evaluating Social
Network Analysis with NodeXL
Elizabeth M. Bonsignore∗§ , Cody Dunne ∗† , Dana Rotman∗§ , Marc Smith¶ ,
Tony Capone , Derek L. Hansen∗‡ , & Ben Shneiderman∗†
∗University of Maryland, College Park, MD 20742
§ Center for Advanced Study of Communities & College of Information Studies {drotman, ebonsign}@umd.edu
† Human-Computer Interaction Lab & Department of Computer Science {ben, cdunne}@cs.umd.edu
¶ Telligent Systems Corporation marc.smith@telligent.com
Microsoft Corporation v-tonyc@microsoft.com
‡ dlhansen@umd.edu 301-405-7622 2118C Hornbake Library
Abstract—Social Network Analysis (SNA) has evolved as a individual roles they might not otherwise see.
popular, standard method for modeling meaningful, often hidden This paper relates the process and early results of 2 user
structural relationships in communities. Existing SNA tools often studies, in which graduate students of information science
involve extensive pre-processing or intensive programming skills
that can challenge practitioners and students alike. NodeXL, an (IS) and computer science (CS) learned SNA concepts and
open-source template for Microsoft Excel, integrates a library of techniques while using NodeXL. Our aim is to focus on the
common network metrics and graph layout algorithms within the unique features that made NodeXL learnable and usable. First,
familiar spreadsheet format, offering a potentially low-barrier- we provide an overview of the NodeXL tool. We also describe
to-entry framework for teaching and learning SNA. We present an emergent research method called Multi-dimensional In-
the preliminary findings of 2 user studies of 21 graduate students
who engaged in SNA using NodeXL. The majority of students, depth Long-term Case studies (MILCs), an ethnography-based
while information professionals, had little technical background approach that seems well-suited to enabling more effective
or experience with SNA techniques. Six of the participants had evaluations of complex visual analytics tools ([3-7]). Next, we
more technical backgrounds and were chosen specifically for discuss our methodology and present visualizations produced
their experience with graph drawing and information visual- by the students. We also describe NetViz Nirvana, layout
ization. Our primary objectives were (1) to evaluate NodeXL
as an SNA tool for a broad base of users and (2) to explore principles that can increase the readability and interpretative
methods for teaching SNA. Our complementary dual case-study power of social network visualizations [3]. As a set of criteria
format demonstrates the usability of NodeXL for a diverse set aspired to by most of the students, we feel it is useful for
of users, and significantly, the power of a tightly integrated the future design of SNA tools in general and NodeXL in
metrics/visualization tool to spark insight and facilitate sense- particular. Finally, we offer lessons learned for educators,
making for students of SNA.
researchers, and developers of SNA tools such as NodeXL.
We report on the process of learning SNA techniques to study
I. I NTRODUCTION
online communities in [4].
The booming popularity of social networking is reflected
in recent statistics from the Pew Internet & American Life A. NodeXL Overview
Project [1]: the number of adult internet users who have NodeXL (Network Overview for Discovery and Explo-
a profile on an online social network site has more than ration in Excel) is an open-source SNA plug-in for Microsoft
quadrupled in the past four years, rising from 8% in early Excel 2007 (http://www.codeplex.com/NodeXL). NodeXL is
2005, 16% in 2006, to 35% in December 2008. Over half of intended to be easy to adopt for existing users of Excel,
those adults maintain more than two online profiles, generally taking advantage of common spreadsheet capabilities such
on different sites. They blog: 54% of college students read as sorting, filtering, and creating formulas. NodeXL extends
blogs and 33% write them; overall, 36% of adults read blogs the spreadsheet into a network analysis and visualization tool
and 13% write them, and almost 20% remix content into their by incorporating a library of basic network metrics (e.g.,
own inventions to be shared online [2]. The widespread use of degree, centrality measures, elementary clustering) and graph
social networking applications has precipitated a greater need visualization features. Data can be entered or imported into
by more diverse users to understand how their online commu- the NodeXL template and quickly displayed as a graph. It is
nities evolve and thrive. SNA tools are not just for scientists uniquely positioned to support the growing number of com-
anymore. Moderators, administrators and other community munity analysts who have neither the time nor desire to step
experts also have a stake in learning more about the structural through static visualizations or to learn complex programming
dynamics of their interactions. The emergent challenge for interfaces. Because it is an evolving open source project, users
designers and educators is to build easy to learn interfaces with programming skills can also access NodeXL compo-
that enable these users to discover community patterns and nents [5].
2. Fig. 1. The NodeXL Workspace. The dual pane view of network metrics (left pane) & network graph (right pane) provide an integrated snapshot of
statistics & visualization, along with built-in functions & controls that support exploration & discovery. Individual worksheets separate network analysis tasks
into separate categories, closely aligned with topology & attribute-based tasks outlined in [6], such as “Edges,” “Vertices,” “Clusters.” The social network
shown reflects voting patterns of U.S. senators, the analysis of which is detailed in [7] and [8].
Arguably, the most significant design model NodeXL im- mation. SNA tools and techniques, then, can be situated at the
plements is an integration of statistical measures and visual- intersection of social computing and visual analytics. While
izations (see Fig. 1). As noted in [7], “SNA is a deductive most SNA tasks can be classified according to a taxonomy
task, and a user’s exploratory process can be distracted by or general workflow ([13], [6]), the actual exploratory process
having to navigate between separate statistical and visual- is dynamic, non-linear, and dependent on contextual factors
ization packages” (p.266). In terms of interaction factors such as network size, complexity and aspects of the network
for visual analytics tools, this design feature is called the being investigated. Typical human-computer interaction tests
“connect” interaction [8]. This connectivity is manifest in the conducted in controlled lab-environments cannot adequately
dynamic linkage between the data spreadsheet view and the reflect the complexity of real-world SNA tasks.
graphic layout view. Activating one representation will cause a Many usability studies of visualization tools have been
simultaneous change in the other [9]. For example, a node and conducted in laboratory settings under controlled conditions
its adjacent edges are highlighted in the graph whether a user ([14], [15]). Conversely, ethnographic studies of technology
clicks on the visual representation of the node in the layout have allowed researchers to gain deep understanding of how
pane or the spreadsheet row for the node in the “Vertices” users interact with technological tools ([16], [17]) in context-
tab. The simultaneous views of network statistics and network dependant conditions [18]. While long term qualitative studies
graphs combine language and visual attention modalities such can be difficult to sustain and may present a challenge to
that each provides “cognitive offloading” scaffolds ([9], [10]), charting consistent usage patterns, they are more aligned
and can trigger associative learning [11]. with the ways users interact with complex analysis tools and
B. Related Work: SNA Tools’ Evaluation & Education engage in complex research problems. The MILCs approach
SNA practitioners explore complex sets of relationships is part of a growing movement in the development of visual
within social systems to discover codifiable patterns of inter- analytic systems that argues against the use of traditional
action or reveal structural signatures of social roles ([5], [12]). prescriptive, task-based evaluations ([14], [15], [7], [8], [19]).
Complex social relationships and community use of social The original MILCs aimed to: (1) help developers design and
media can become visible with SNA. Similarly, in the field refine information visualizations systems more effectively and
of visual analytics, complex, dynamic data sets are explored (2) spark expert users to unexpected insights and discoveries.
for unexpected insights and synthesized into actionable infor- Previous evaluations of visual analytic systems such as
3. SocialAction ([7], [13], [8]) and others (detailed in [20], ways students could study social interactions. This perspective
[19]) invited domain experts to evaluate the system. The situated SNA within an overall learning environment in much
same model can be applied to the evaluation of complex the same way MILCs have situated visual analytics tools
visualization systems used by teachers and students. Just as within the work environments of their domain experts.
exploratory efforts in scientific inquiry can be mapped to a Before instruction, IS users’ level of Excel and SNA lit-
cycle of sense-making tasks with feedback at each level [21], eracy was assessed by a self-reporting questionnaire. Most
exploratory learning is a cycle of sense-making tasks with students reported a basic competency level in Excel and almost
multiple feedback loops [11]. An early example of the MILCs none of them had any prior knowledge of SNA. Their first
paradigm in educational contexts is detailed in [22]. Of note, encounter with NodeXL was a tutorial session conducted
its non-structured observation format empowered students to in a computer lab, following the outline and examples of
explore, and in many cases reach new insights (“discovery Network Analysis with NodeXL: Learning by Doing, a draft
learning,” [22]). A main advantage of extending the MILCs introduction to SNA text written by 3 of the authors. The
approach to learning contexts is that it offers students the tutorial tasks supported 4 of the 7 workflow steps outlined
same evaluation and design affordances it holds for research in [13], and reflected the general topology- and attribute-based
with domain experts: the MILCs process reflects the real tasks presented in [6]’s task taxonomy for graph analysis.
way students explore an interface as they are learning SNA During the study, participants engaged in weekly class discus-
concepts. sions and completed 3 assignments between classes. The first
In related work on SNA education, [23] summarized the assignment was based on a pre-defined dataset while follow-
results of a course on optimizing the online communication on assignments allowed participants to independently explore
behavior of student teams collaborating across geographic SNA-related research questions on their own datasets. The
distances while studying SNA. Insofar as one-third of the students were observed as a group during class sessions, but
course presented social network analysis concepts, its format each also had a 1-hour one-on-one session with a researcher.
resembled the seminar we studied. In [23]’s case, the students The individual sessions followed a contextual inquiry protocol,
collaborated on course work while applying SNA principles in which participants ’talk-aloud’ as they work, while the
to evaluate their own communication patterns. In contrast, our observer asks clarifying questions and makes note of important
intent was to evaluate how an interface like NodeXL can be occurrences [24]. Self-reporting diaries are an integral part of
used as both an SNA tool and a means to teach SNA. the MILCs approach [15], and also allowed participants to
capture and reflect upon their actions, emotions, and moments
II. M ETHODOLOGY – T WO - PRONGED A PPROACH of insight ([25], [26]).
As noted in §I-B, we used a qualitative approach, based pri-
B. Methodology: CS users
marily on MILCs [15]. MILCs employ multiple user analysis
methods within the same case study to attain a rich evaluation We used similar techniques as detailed above (§II-A) for 6
of the researched tools. In order to conduct an evaluation CS students, compressed into a single 90 minute session for
of NodeXL sensitive to diverse categories of users (IS & each. Two self-reporting questionnaires were given to each
CS students), we designed a two-pronged appraisal approach. participant before any instruction took place: one a subset of
For each group of users we used a core set of instruments the Excel and SNA literacy survey taken by our IS users,
and techniques: self-reporting pre-survey, tutorial session with and the other part of the standard NodeXL network analysis
NodeXL; observation/interview, and post-survey. However, the survey optionally taken by new users of the tool. The latter
length and depth-of-focus of our evaluations were tailored to deals more specifically with the user’s experiences with other
the background characteristics of each group. Because the IS network analysis tools and their academic background. As
users were using NodeXL as part of a semester-long course, they were more adept with both SNA tools and techniques,
we could follow the MILCs model with them, making in-situ we favored one-on-one tutorials with the CS users in lieu of
observations over time and allowing them to reflect on their class instruction. This format also allowed them more freedom
use of the tool as they grew more proficient. We used a more to ask questions and critique NodeXL as new features were
compressed but focused evaluation for the CS users, as they introduced. The tutorial followed the same outline used by the
required significantly less time to learn NodeXL. Although the IS users. In the second half, the CS users were asked to select
detailed methodologies employed for each group differed, we a dataset of interest to them out of the ones we had available or
believe our two-pronged approach enabled a rich, thorough their own collection and to identify and highlight three things
evaluation of NodeXL as a tool for a broad base of users. within it:
• a unique social role within the network,
A. Methodology: IS users • an interesting sub-group within the larger network, and
Over a 5 week period, 15 IS graduate students participating • anything else of particular interest.
in a “Communities of Practice” seminar were introduced to During their analysis, which lasted 30-45 minutes, we
SNA and used NodeXL to explore SNA fundamentals. While observed each participant individually in the same manner
SNA was presented as a powerful set of techniques for analyz- as the IS users. Their comments, along with each step they
ing online communities, it was considered only one of many took in their analysis and any problems they encountered were
4. However, a tool that enables unrestricted manipulation of
these attributes can serve as a double-edged sword. While
it allows users to create complex visualizations of multi-
dimensional data, over-use of multiple display options can
quickly reduce the graph’s readability and obscure potentially
significant perspectives. To ensure that students maintained a
high awareness of the importance of producing readable graphs
that accurately reflect the community relationships they hoped
to convey, the students read and discussed the readability
metrics (RMs) outlined in [3]. They were enjoined to aspire
to the four principles of NetViz Nirvana [3]:
• Every node is visible
• Every node’s degree is countable
• Every edge can be followed from source to destination
• Clusters and outliers are identifiable
In-class and online discussions about NetViz Nirvana, cou-
pled with NodeXL’s tight integration of statistics views and
graph layouts empowered the students to produce relatively
Fig. 2. Student Product: The friendship relationships of an online com- sophisticated SNA graphs in a short period of time. Most
munity. The visualization uses multiple SNA metrics to reveal structure: students took full advantage of the ease with which NodeXL
node color reflects clustering coefficients (identifying possible subgroups and
their connectivity to the whole); edge colors reflect geographic connections. can map individual and collective metrics to the shape, size,
Node radius reflects betweenness centrality (i.e., bridges/boundary spanners and color attributes of visual graph objects. A sample of
for the community overall), and shape reflects either closeness centrality or student products, along with captions detailing their layout
eigenvector centrality (no nodes met both metrics at the same time). Those
members with high closeness centrality are triangles; those with low closeness choices, are shown in Figures 2–5. In each case, the way
centrality are circles; those connected to the most popular members are NodeXL supports thoughtful use of shape and color, as well
squares. as selective labeling of nodes of interest, helps direct attention
to important relationships.
The student who created Fig. 2 used multiple SNA metrics
noted by the observer so as to form an approximation to the to reveal her community’s structure (see caption for details).
self-reporting diaries used for the IS users. Personal diaries The layout she chose also follows many NetViz Nirvana
and class assignments were omitted due to the experience design principles, as larger, more connected nodes are pulled
and technical background of the CS users, as well as the into the layout’s center. The student who created Fig. 3
more time limited nature of the study. However, this one-on- focused on identifying the relationship between 3 community
one perspective afforded us an increase in detailed responses forums. Like the Fig. 2 author, this student also mapped
from each individual. At the conclusion of the second half metrics (degree & tie strength) to visual properties (diameter
participants completed 2 additional questionnaires: one being & edge thickness), using her graph results to identify boundary
the remainder of the standard NodeXL survey mentioned spanners in the community and highlight a close connection
above that deals with the user’s experience with NodeXL and between two sub-groups. Again, the use of shapes and color
the other select questions from the self-reporting diaries used were used to draw attention to important individuals, supported
for the IS users. by the ability to label only select nodes. Another student
III. N ODE XL & SNA S ENSE M AKING applied a simple NodeXL “skip” filter to model a complex
community management dilemma: how to find the best can-
As noted in [3], Euclid established that a point is that didate to replace a departing administrator (Fig. 4 & 5). He
which has position but no magnitude. He also defined a line hypothesized that his admin and experienced community mem-
as that which has position and length, but no breadth [27]. bers would have higher eigenvector and betweenness centrality
While these axioms are applicable in plane geometry and measures, and reflected these metrics in visual properties.
algorithms historically used to display graphs [3], nodes and Using NodeXL’s “skip” filter to remove the existing admin, he
edges in social networks must convey the same attributes as was quickly able to model and “see” which member possessed
the people and relations they represent. Practitioners who wish the best network values to take over (see captions for details).
to explore community member characteristics such as number
of contributions, gender, or degree (i.e., number of friends) IV. A SPECTS OF N ODE XL U SABILITY
may want to represent them visually via multiple node and Of our 6 CS users, 5 had a course background in network
edge attributes such as shape, size and color. The ability to visualization, and 4 also had general information visualization
apply a variety of visual features to SNA data elements based course experience. Only one participant lacked both. Three
on various individual and community metrics was a NodeXL of the participants had studied SNA as part of a course, and
feature that all students used and enjoyed. one has presented network analysis findings in both academic
5. updating, instead preferring to make all the data modifications
beforehand and visualizing the data only as a final step.
Conversely, this real-time updating was the most appealing
aspect to 3 of our users, who also requested that “autofill
columns” immediately update the graph pane with the results
instead of only placing them in the workbook. This feature,
as well as the optional disabling of real-time updating, has
been added to NodeXL. Though it would further increase the
response time, 2 users also asked that the autofilled columns
to optionally stay in sync with their source data.
Another key complaint is the desire for increased usability.
Our participants expected many operations to be available
directly from the context menu of the graph pane, including
fixing vertex placement, deletion of vertices and edges, and
skipping filtered out vertices and edges after using dynamic
filters. Further, users found the NodeXL ribbon tab to be
confusing and too detailed, instead requesting simpler, larger
Fig. 3. Student Product: This graphic helped the student identify key icons that give precedence to frequently used features. The
boundary spanners (e.g., ateixeira), recognize the close connection between 2 latest version of NodeXL has implemented part of an interface
different forums, and the lack of significant activity by the official community
leaders. Blue triangles are community leaders; green diamonds are hosts. She overhaul designed to address these issues, with basic features
could also easily label nodes selectively. However, she did have to manually exposed in the ribbon tab and grouped in a more orderly
create a separate legend to explain these roles and relationships. fashion while advanced ones are moved to pop-up dialog
boxes. CS users also requested shortcuts for many existing
functions, some of which we have already implemented.
and business settings. Thus, with CS users, an emphasis was For some datasets, users hit the limits of edge aggregation
made on specific usability aspects of the tool. However, these within NodeXL. Two of our users requested more expressive
aspects were also correlated against IS users’ feedback. We aggregation techniques than only a count of the number of
categorized the CS user surveys and comments according aggregated items, instead preferring the option of selecting a
to the tutorial outline described in §II-B, with additional measure such as sum, median, or mean for the weight column.
areas added as needed for comments that defied classification. Another user wanted a way to easily merge vertices while
Overall, we found 16 unique acclamations of NodeXL across aggregating the data columns.
10 aspects of the tool, 5 of which were were outside the Finally, when our participants wanted to output the results of
scope of the tutorial document. Moreover, we found 96 unique their analysis they found the existing image export features of
criticisms and feature requests across 37 areas, which were NodeXL lacking. Specifically, many requested vector graphic
substantially more specific than the acclamations. 17 of the formats such as SVG, EPS, and WMF to output publishable
37 areas were mentioned during the IS users’ tool exploration, images. We added an XPS export to satisfy this need. To im-
not instruction. Of the 16 acclamations, 3 were made by 2 or prove text legibility and reduce file size they suggested a com-
more of the participants. pressed, lossless format like PNG replace NodeXL’s default
The interaction between the workbook and the graph pane in image export options (JPEG/lossy and BMP/uncompressed).
NodeXL was quite appealing to the CS users, as they wanted to All participants rated their satisfaction with NodeXL higher
maintain a separation of data and visualization while enabling than current network analysis software except for one, who
connections through brushing and linking. One user especially rated NodeXL equivalent to current software. This participant
liked the ability to use cell referencing within the worksheets had the weakest background in Excel, SNA techniques, and
to define tunable constants for their vertex attribute formulas, alternative SNA tools among those studied.
something he had not been able to do in other tools. Of the
criticisms and feature requests, 11 were brought up by at V. L ESSONS L EARNED FROM VARIOUS P ERSPECTIVES
least 2 participants and 2 were mentioned by at least 3 of The process of evaluating visual analytics tools involves
them. The foremost concern our CS users had was the lack the intersection of learning a new, complex system, learning
of responsive controls at all times during their analysis. While the language of complex SNA concepts, and applying these
running the layout algorithms, 3 users requested a progress components to abstract goals. How do you learn to explore
indicator with stop or pause buttons, even though the graph and interpret patterns of interaction in online communities to
pane is updated after every iteration. Moreover, the brushing reveal latent social structures or signature roles? [12] Our study
and linking between the workbook and visualization slowed provides a first look at the impact a tool like NodeXL may have
down 2 users who worked with large datasets, as selecting on the teaching and learning of fundamental SNA concepts,
many vertices in either caused a substantial update in the other. and the benefits of using MILCs as an evaluation method
One participant even requested the ability to disable real-time for the learning process. Our two-pronged methodology for
6. into one integrated visual analytics tool was effective in
empowering new users of SNA tools to create quite sophisti-
cated, meaningful social network diagrams. Most students took
advantage of the close coupling between spreadsheet metrics
and the visualization to manually manipulate their layouts,
moving fluidly between 1) finding a node in the spreadsheet
to maneuver it on the graph pane, and 2) selecting a node
in the graph to review statistics for it from the spreadsheet.
This interaction was cited by the CS users as a key feature
of NodeXL. The Communities of Practice instructor found it
useful during teaching to highlight SNA concepts as they were
presented on the classroom projector. However, the “connect”
interaction paradigm designed into NodeXL did not extend to
the automatic creation of labels or legends for visualizations
created by users – a feature that is standard in chart features
found within Excel. Most students had to create their own
legends for nodes (see Fig. 3). This failing has been addressed
in an upcoming release.
Fig. 4. Student Product: The student confirms a hypothesis that the
Filter: NodeXL’s suite of “filter” interactions seems to be
community administrator and more experienced members will have high missing a classic “details-on-demand” feature. Several students
eigenvector centrality (connectedness, represented by node radius) and be- commented that in order to make side-by-side comparisons
tweenness centrality (bridging, represented by node color). He takes this idea
one step further in Fig. 5.
of columns of interest in the worksheets, they had to scroll
back and forth constantly. At times, their efforts to cut-and-
paste user-created columns near built-in columns to simplify
comparisons caused unexpected results (either human error in
the “cut-n-paste,” or a lack of awareness about the inability
to mix certain types of columns in NodeXL). Based on
informal feedback with NodeXL developers, such straight-
forward comparisons are possible, but require multiple steps.
A macro could be created as a shortcut, following the familiar
“cut-n-paste” paradigm. Or, in some cases, the “freeze panes”
feature could be used more effectively.
Encode: The “encode” interaction, or the ability to apply
shapes, size, labels, orientations by encoding data elements
with attributes of interest, is the most popular way in which
students chose to represent their communities. NodeXL offers
an impressive array of visual attributes to represent SNA
metrics. Variations in node/edge color, size and shape were
used extensively by students to improve the interpretive and
expressive power of their visualizations. Most students used
relatively simple metrics like degree for nodes and tie-strength
Fig. 5. Student Product: Modeling which node would be the next-best for edges. Five of the 12 IS users felt their use of extensive
candidate to serve as community administrator. To simulate this management metrics was limited due to the structure of the data they
dilemma, statistical data about the current admin was ’skipped’ via NodeXL
filtering functions, & the next best “node” pops up in his visualization. collected – many of the students chose to collect data about
community member’s connections to community artifacts,
such as contributions to various forums (see Fig. 3), and in
studying different types of users has also enabled us to offer one case, time-stamps. In such cases, metrics are not easily
a robust evaluation of NodeXL as a visual analytics tool. The interpreted or may not apply.
guidelines for interaction design proposed by Ji Soon Yi (cited Explore: The students enjoyed using attribute rankings to try
in [8]), such as “connect,” “encode,” “filter” and “explore” to make their graphs more readable. However, in their effort
serve as our evaluation criteria, as described in the following to reach NetViz Nirvana, they still had to invest in a great deal
lessons learned. of manual manipulation. As noted in [3], automated layout
algorithms are not sufficient to effectively represent the range
A. Lessons Learned for Designers of attributes and often localized structures that most SNA
Connect: The “dual-front approach,” or “connect” inter- experts and students would like to highlight. Based on the
action design [8] that combines statistics and visualizations exploration process followed by many of the users, we believe
7. the addition of specific RM interactions into the design of SNA semi-automatic “Calculate Metrics” function, a powerful li-
tools would be of great value. Specifically, node RMs such brary of basic techniques, often hid some of the instructional
as node occlusion and node size, shape, and color variance power of statistics from the students. Interaction functions to
constraints should be the highest priority (a potentially positive allow metric tweaking and graph response, along with interac-
side effect of incorporating these node RMs is they may tive RM layout functions could enable “teachable moments”
reduce the risk of edge tunnels). An interactive edge RM that make SNA metrics explicit, guide the user through rec-
for reducing edge crossings would also be of immediate ommended steps required to attain NetViz Nirvana, and allow
value, as many students used edge thickness to reflect tie- further opportunities for experimentation and exploration to
strength. Both IS and CS users alike had difficulty using labels reach novel insights.
effectively, underscoring the potential benefits of node RM Tutorial tasks did not take advantage of the “reconfigure”
research efforts. interaction design [8], i.e., offering different perspectives on
The ability to help users track their exploration process, or the same data. NodeXL is embedded within Excel, which is
navigate through a history of their actions is also a component equipped with multiple existing charts that can be exploited
of the “Explore” interaction. Currently, NodeXL does not to display data through different lenses (e.g., scatterplot,
contain any supports for “undo” or traceable histories of histograms). Emphasis was placed on creating and analyzing
exploration or annotation paths. Students were quite frustrated network views or sorting the tabular spreadsheet to find
with the interruptions they faced when the tool crashed, and interesting patterns. Time constraints within the course, along
often, “had to start over.” SocialAction is one SNA tool that with student lack of in-depth familiarity with Excel’s features
can serve as an example in adding these features to the design reduced opportunities to experiment with and learn from a
queue [13]. An “Undo/Redo” function would appear to be greater variety of perspectives.
especially useful in beta-testing contexts, when users who The ability to keep track of actions is a scaffolding sup-
experience multiple system crashes are supported by some port that enhances learning, confidence and enables extensive
means for reverting back to a recent “safe” mode. (Note: a freedom to explore. As noted in §V-A, an “undo” feature for
few users successfully used Excel’s crash recovery feature to adjustments made to either the spreadsheet view or the layout
resume analysis.) of nodes should be added. A work-around several students de-
veloped for lack of this function was to keep track of multiple
B. Lessons Learned for Educators/Tutorial Designers saved versions of their work, or maintain various simultaneous
NodeXL was found to be a relatively easy to learn tool. Excel spreadsheets that reflected their workflow status. These
Excel experience and familiarity with graphs and networks additional files required explicit tracking, which may have
appear to be baseline skills for effective use of NodeXL. Lack increased the cognitive load the students experienced.
of in-depth knowledge of Excel proved to be the primary
barrier to the learning process for IS users. Three of the C. Lessons Learned for Researchers
CS users and 5 of the IS users felt their Excel experience MILCs are indeed difficult to execute and analyze [28], due
was useful, while 8 of the IS users felt their inexperience to amounts of data collected, and the amount of time invested
with Excel, especially a lack of expertise in creating Excel by both participants and researchers. However, they provide
formulas, prevented their rapid adoption of the full features of more accurate representations of the processes experts and
NodeXL. learners follow while exploring complex data sets, and can
Pacing issues, both in the tutorial task flow, lab sessions, and result in more meaningful evaluation measures and recommen-
assignments, were of paramount concern to the IS users, who dations for improvement in design and teaching approaches.
were asked to learn the language of SNA, common statistical Exploratory learning can be described, fundamentally, as a
and graph-based approaches, and a new tool in a relatively cycle of exploration, insight (learned concept), and deeper
short amount of time. Users had less than 2 weeks to learn the exploration. Consequently, it is difficult to shoe-horn into
basics of NodeXL and relevant SNA metrics, then to interpret specific tasks – much like the exploratory analysis processes
their first set of graphs, starting with a pre-defined data set followed by experts. MILCs are thus an effective, rich infor-
that did not reflect their communities of interest. Nine of 12 mation source for visual analytics usage and learning. Overall,
IS users noted that the tutorial and first assignment made too this study not only adds to the growing body of research
steep and quick a transition from small networks of relatively establishing MILCs as an effective evaluation method for
low graph density to large networks of high graph density. visual analytics tools, but also extends it as a valid approach
Similarly, our CS users emphasized the importance of a series for evaluating systems designed for exploratory learning,
of small tasks with predefined goals, especially when the users specifically those designed for a wide range of learners.
do not have a vested interest in the dataset [28]. Previous MILCs and a field study on exploratory learning
Promoting user awareness of layout considerations such as ([19], [26]) captured moments of insight via “Eureka” reports.
NetViz Nirvana resulted in a high level of reflective thought In these early studies, users not only recorded the analysis
and effort put forth by IS users in their analyses. Thus, steps they followed, along with times-on-task for major activ-
incorporating NetViz Nirvana functions such as real-time RM ities, but also reflected upon key insights they experienced. In
interactions may support educational goals for SNA. NodeXL’s future research applying MILCs to teaching/learning contexts,
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