Information Visualization Alvitta Ottley Washington University in - - PowerPoint PPT Presentation

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Information Visualization Alvitta Ottley Washington University in - - PowerPoint PPT Presentation

CSE 557A | March 01, 2018 Information Visualization Alvitta Ottley Washington University in St. Louis Slides credit: Mariah Meyer, University of Utah Gra Graph Da Data Gra Graph Da Data What can you represent as a graph? De Definition


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Information Visualization

Alvitta Ottley Washington University in St. Louis CSE 557A | March 01, 2018

Slides credit: Mariah Meyer, University of Utah

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Gra Graph Da Data

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Gra Graph Da Data

What can you represent as a graph?

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De Definition

Graphs represent connections or relationships

  • Social network
  • Software execution (call graph)
  • Gene expression
  • Financial transactions
  • WWW
  • US telephone system

One of the oldest and most studied areas of information visualization

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Wh What at Mak akes es a a Grap aph?

Node-Link Diagram

  • Vertices (nodes)
  • Edges (links)

Adjacency Matrix:

  • 1: 2
  • 2: 1, 3
  • 3: 2
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Ad Adjace cency ncy Matrix ix

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Le Les Mi Misé sérables Ch Characters Co Co-occu

  • ccurrence

ce

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Id Identifying pa g patterns

Henry 2006

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Ca Can you think of sho shortcomings o s of t thi his s ap approach ach?

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No Node-Li Link D Diagrams

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Te Term rminology

  • Directed vs. Undirected
  • Cyclic vs. Acyclic
  • Degree of a vertex
  • In-degree
  • Out-degree
  • Weights on edges
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Te Term rminology

  • Directed vs. Undirected
  • Cyclic vs. Acyclic
  • Degree of a vertex
  • In-degree
  • Out-degree
  • Weights on edges

1 3 2 1 3 2

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Te Term rminology

  • Directed vs. Undirected
  • Cyclic vs. Acyclic
  • Degree of a vertex
  • In-degree
  • Out-degree
  • Weights on edges

1 3 2 1 3 2

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Te Term rminology

  • Directed vs. Undirected
  • Cyclic vs. Acyclic
  • Degree of a vertex
  • In-degree
  • Out-degree
  • Weights on edges

1 3 2

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Te Term rminology

  • Directed vs. Undirected
  • Cyclic vs. Acyclic
  • Degree of a vertex
  • In-degree
  • Out-degree
  • Weights on edges

1 3 2

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Mo More T Termi minolo logy gy

  • Centrality Measures:
  • Degree Centrality
  • How many neighbors does a vertex have?
  • Betweenness Centrality
  • How often does a vertex appear in paths between other nodes?
  • Closeness Centrality
  • How quickly can a node reach all other nodes in the graph?
  • Eigenvector Centrality
  • Google PageRank (assumes directed graph)
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Gra Graphs vs. Tre rees

  • Tree is a special case of a general graph
  • There are no cycles in a tree
  • Edges are (usually) directed or are implicitly directed
  • Special designations for root, leaves, etc.
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Cha Challenge ges i s in G Graph V h Visu sualiza zation

  • Graph layout and position
  • Related to your studio!
  • Navigation / Interaction
  • How to support a user in understanding all the relationships in the graph
  • Scale
  • What happens if the graph has 10 nodes? 1,000 nodes? 1,000,000 nodes?
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Co Comparing Representations: Which do you pre prefer r and d why?

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De Dealing wi with lar large an and d me messy graph phs

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Te Techniques for r Gra raph Simplification

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Mo Motif G Gly lyph phs

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Mo Motif G Gly lyph phs

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Ed Edge Bundlin ing

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Wh What t are e th the e str tren ength ths and d wea eakn knes esses es of th thes ese e approaches es?

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How How do

  • you
  • u

cho choose se a a lay layout?

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Hi Hierarchy

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Ge Geos

  • spatial
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Wh What t if th there is is n no in intrin insic ic la layout? ut?

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Fo Force ce-di dire rected d layout

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Fo Force Model

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Algo Algorit ithm

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d3 d3 example

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St Studio: io: Su Suppor

  • rtin

ing I Interaction ion a and Und Understand nding ng.

How would you add interaction to a force- direction graph?