X. Network data visualization

Network data

• Networks/graphs, denoted as G = (V,E) are structures formed by a set of vertices V (aslo called nodes) and a set of edges E, that are connections between pair of vertices.
• Tree: special case, no cycle, one parent per node

Network vis

• For network data without spatial (geometrical) information we can control visualization layout.
• A primary concern of tree/graph drawing is the spatial arrangement of nodes and edges.
  • Often (but not always) the goal is to effectively depict the graph structure: Connectivity, path-following - Topological distance - Clustering / grouping - Ordering (e.g., hierarchy level)
  • Other tasks can be related to attributes

Network tasks: attribute-based vs topology-based

Topology based tasks

• Find paths
• Find (topological) neighbors
• Compare centrality/importance measures
• Identify clusters / communities

Attribute based tasks

(similar to table data)

• Find extreme values

Combination tasks - incorporating both

Example: locate - find single or multiple nodes/links with a given property

• Topology: find all adjacent nodes of given node
• Attributes: find edges with maximum edge weight

Network Visual Encodings: Node-link diagram

• Nodes: point marks
• Links: line marks
  • Straight lines or arcs
  • Connection between nodes
• Inituitive and familiar
  • Most common
  • Many, many variants

Criteria for good node-link layouts

1. Minimize

   • edge crossings
   • distances between topological neighbor nodes
   • total drawing area
   • edge bends
   • edge length disparities (sometimes)

2. Maximize

   • angular distance between different edges
   • aspect ratio disparities

3. Emphasize symmetry

   • similar graph structures should look similar in layout

→ Criteria conflict