Abstract: Graph visualization is a powerful way to represent complex connected data and help viewers quickly understand relationships and patterns between different entities in the network. Graph visualization helps to provide insight into the structure of data, which can be useful in tasks in the area of networking, bioinformatics, web design, machine learning and other domains that require visual representation of relationships.
By completing this workshop, you will learn how to create compelling visualizations, using directed and undirected graphs, dynamic graphs, and clustering. You will also learn about centrality metrics and network density. Additionally, you will learn about different layout algorithms, as well as the strategies for interpreting and communicating the graph data in meaningful ways.
Lesson 1: Graph visualization overview. Explore directed and undirected graphs. Learn about centrality metrics.
Lesson 2: Graph visualization and exploration of results. Create a graph. Find clusters in your data. Identify influencers and gatekeepers. Explore ego networks.
Lesson 3: Dynamic graphs. Create a dynamic graph to observe how relationships between objects change over time.
Gephi open-source software
Bio: Tamilla Triantoro is an Associate Professor of Computer Information Systems at Quinnipiac University and a leader of the Masters Program in Business Analytics. She was previously an Academic Director of Data Analytics at the University of Connecticut. Dr. Triantoro is an author, speaker, researcher, and educator in the fields of artificial intelligence, data analytics, user experience with technology, and the future of work. She received her Ph.D. from the City University of New York where she researched online user behavior. Dr. Triantoro presents her research around the world, attempting to demystify the complexity of today's digital world and to make it understandable and relevant to business professionals and the general audience.
Tamilla Triantoro, PhD
Associate Professor of Computer Information Systems | Quinnipiac University