Network Visualization & Interaction Jay Thom

Outline Introduction Background Related Works Methodology Conclusion Why is this important? Background History Related Works Problems Solutions Methodology What I will contribute Conclusion

Why Is This Important? My motivation: Internet Topology Measurement Our project Dealing with large amounts of data How do we interpret it? Introduction * Background * Related Works * Methodology * Conclusion

Generating Massive Data in Daily Life… Finance Healthcare Sensor Networks Power Grid National Security Consumer Data 50,000 GB per second! Data is meaningless unless we can interpret it! Introduction * Background * Related Works * Methodology * Conclusion

How Humans Assimilate Data… Introduction * Background * Related Works * Methodology * Conclusion

Much Data Representable as Networks We need to interpret data to make it useful: Automated algorithms Data mining Machine learning Human domain expertise needed for optimization: Data needs to be interpreted quickly Inconsistencies need to be identified We need to combine automation with interaction Introduction * Background * Related Works * Methodology * Conclusion

History of Data Visualization Charles Joseph Minard (1781-1870), Napoleon’s Grand Army in Russia Introduction * Background * Related Works * Methodology * Conclusion

Modern Network Graph Visualizations The Internet Introduction * Background * Related Works * Methodology * Conclusion

Problems…Readability Ben Shneiderman and Cody Dunne; ”Interactive Network Exploration to Derive Insights” Readability Metrics: Crossing number number of times an edge crosses another edge Drawing area (smallest bounding box) smallest possible bounding box looks best Symmetry groups find symmetry in the graph and work around it Minimal bends in edges keep edges as straight as possible Minimal edge lengths Minimize total length of edges Angular resolution higher angle resolution is desirable Slope number use minimal number of distinct edge slopes Introduction * Background * Related Works * Methodology * Conclusion

Readability - Motifs N. Karracher et al: ”Visual Techniques to Support Exploratory Analysis of Graph Data” Cliques Connectors Fans Introduction * Background * Related Works * Methodology * Conclusion

Readability - Motifs Introduction * Background * Related Works * Methodology * Conclusion

Problems…Layout Yifan Hu; “Algorithms for Visualizing Large Networks” Not all graph can be aesthetically embedded into two or three-dimensional space small world graphs Graphs exhibiting power law degree distribution * Graphs of this nature require an interactive system to comprehend and explore Spring-Electrical Model Force-directed Quad-tree Stress-Strain Models Stress model Strain model (classical MDS) Multi-scale algorithm Landmark MDS Pivot MDS Introduction * Background * Related Works * Methodology * Conclusion

Layout – Spring Electrical Models Force-Directed: Highly scalable, can lay out millions of nodes in minutes Does not cope well with pre-defined edge lengths Introduction * Background * Related Works * Methodology * Conclusion

Layout – Spring Electrical Models Quad-Tree: Simulates Force-Directed model Useful when there are vertices far from the main body of the group Introduction * Background * Related Works * Methodology * Conclusion

Layout – Stress/Strain Models Stress Model: Better than Force-Directed for pre-defined edge-lengths Spring forces attach each node to its neighbors Ideal spring length is pre-defined Introduction * Background * Related Works * Methodology * Conclusion

Layout – Stress/Strain Models Pre-dates the Stress model Fits to the inner product of positions Centers nodes around an origin Introduction * Background * Related Works * Methodology * Conclusion

Problems…Interaction Question: What is interaction? Ji Soo Yi et al; “Toward a Deeper Understanding of the Role of Interaction in Information Visualization Interaction Categories: Select Explore Reconfigure Encode Abstract/Elaborate Filter Connect Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Select “Mark something as interesting” Allows user to mark a data item to keep track of it Item is highlighted or labeled, and can be easily located after rearrangement Preceding action to subsequent operations Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Explore “Show me something else” Rearrange the view to bring out different details Move smoothly from one view to another Pan or zoom a view to enhance detail Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Reconfigure “Show me a different arrangement” Change the spatial arrangement of representations Reveal hidden characteristics of data Provide different perspectives Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Encode “Show me a different representation” Change the way data is represented Uncover new aspects of relationships between data points Change colors, shapes, sizes, chart/graph types Introduction * Background * Related Works * Methodology * Conclusion

Interaction – Abstract/Elaborate “Show me more or less detail” Adjust the level of abstraction Alter representation from overview to detailed view of individual cases Show sub-trees of a tree Show tool-tip view (example) Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Filter “Show me something conditionally” Enable user to change set of data items being presented based on some condition Show only data of a certain range Change color of filtered data so that it is still visible but discernable from non-filtered data Introduction * Background * Related Works * Methodology * Conclusion

Interaction - Connect “Show me related items” Highlight associations and relationships between items Show hidden items relevant to a specified item Hover over an item to reveal hidden relationships Click on item to show item details not visible Introduction * Background * Related Works * Methodology * Conclusion

U.S. Senators by Voting Similarity 2014 Introduction * Background * Related Works * Methodology * Conclusion

U.S. Senators by Voting Similarity 2014 Introduction * Background * Related Works * Methodology * Conclusion

Visualization Tools Open-Source Graph Visualization Tools: Guess Gephi NodeXL Cytoscape NetworkX iGraph SNAP Sigmajs D3.js Linkurious R Large Graph Layout (LGL) Cuttlefish Graphistry Graph-tool libSNA MeerKat Netlytic NetworKit Pajek SocNetV Socioviz Statnet SUBDUE Tulip Visone GraphChi Graphviz JUNG Introduction * Background * Related Works * Methodology * Conclusion

Categorization User interface Programming language Ease or difficulty to use Range of tools available Range of interaction available Visual quality Limitations on number of nodes/edges Overall performance Introduction * Background * Related Works * Methodology * Conclusion

Conclusion Why is this important? History Problems Solutions What I will contribute Introduction * Background * Related Works * Methodology * Conclusion