Lecture 12: Network Visualization

Lecture 12: Network Visualization Slides are modified from Lada Adamic, Adam Perer, Ben Shneiderman, and Aleks Aris

Outline • What is a network? • How do you analyze networks today? • What are the challenges? • How to integrate with other methods?

node • edge What are networks? • Networks are collections of points joined by lines. • “Network” ≡ “Graph”

Network elements: edges • Directed (also called arcs) • A -> B • A likes B, A gave a gift to B, A is B’s child • Undirected • A <-> B or A – B • A and B like each other • A and B are siblings • A and B are co-authors • Edge attributes • weight (e.g. frequency of communication) • ranking (best friend, second best friend…) • type (friend, relative, co-worker) • properties depending on the structure of the rest of the graph: e.g. betweenness

Planar graphs • A graph is planar if it can be drawn on a plane without any edges crossing

#s of planar graphs of different sizes 1:1 2:2 3:4 4:11 Every planar graph has a straight line embedding

Trees • Trees are undirected graphs that contain no cycles

Cliques and complete graphs • Kn is the complete graph (clique) with K vertices • each vertex is connected to every other vertex • there are n*(n-1)/2 undirected edges K5 K3 K8

Why Visualization? Use the eye for pattern recognition; people are good at scanning recognizing remembering images Graphical elements facilitate comparisons via length shape orientation texture Animation shows changes across time Color helps make distinctions Aesthetics make the process appealing http://amaznode.fladdict.net/ http://www.touchgraph.com/TGAmazonBrowser.html

Graph Drawing Aesthetics • Minimize edge crossings • Draw links as straight as possible • Maximize minimum angle • Maximize symmetry • Minimize longest link • Minimize drawing area • Centralize high-degree nodes • Distribute nodes evenly • Maximize convexity (of polygons) • Keep multi-link paths as straight as possible • … Source: Davidson & Harel

Node Placement Methods • Node-link diagrams • Force-directed • Geographical maps • Circular layouts • One or multiple concentric • Temporal layouts • Clustering • Semantic Substrates

Force-directed Layout • Also known as: Spring • Spreads nodes • Minimizes chance of node occlusion

Geographical Map • Familiar location of nodes

Circular Layouts (1 circle) • Ex: Schemaball • Database schema • Tables connected via foreign keys

Circular Layouts (concentric) Radial Tree Viewer

Circular (concentric) & Temporal Hudson Bay Food Web

Temporal Layout

Clustering

Hierarchical Clustering

Semantic Substrates • Group nodes into regions • According to an attribute • Categorical, ordinal, or binned numerical • In each region: • Place nodes according to other attribute(s)

Force-directed >30% Circular Layout ~15% Familiar Layout ~30% Statistics on Strategies Node layout strategy First 100 in visualcomplexity.com

Outline • What is a network? • How do you analyze networks today? • What are the challenges? • How to integrate with other methods? http://graphexploration.cond.org/index.html

Challenges of Network Visualization • Basic networks: nodes and links • Node labels • e.g. article title, book author, animal name • Link labels • e.g. Strength of connection, type of link • Directed networks • Node attributes • Categorical (e.g. mammal/reptile/bird/fish/insect) • Ordinal (e.g. small/medium/large) • Numerical (e.g. age/weight) • Link Attributes • Categorical (e.g. car/train/boat/plane) • Ordinal (e.g. weak/normal/strong) • Numerical (e.g. probability/length/time to traverse/strength)

C1) Basic Networks (nodes & links) • Power Law Graph • 5000 nodes • Uniformly distributed

C1) Basic Networks (continued) • Social friendship network • 3 degrees from Heer • 47,471 people • 432,430 relations

C2) Node Labels • Adding labels • Nodes overlap with other nodes • Nodes overlap with links 250 nodes

C3) Link Labels • Challenges: • Length • Space • Belongingness • Distinction from other labels & other types of labels

C4) Directed Networks • Direction • arrows • labels • Thickness • color SeeNet, Becker et al.

C5 & C6) Node & Link Attributes • Types: • Categorical (e.g. mammal/reptile/bird/fish/insect) • Ordinal (e.g. small/medium/large) • Numerical (e.g. age/weight) • Value of node attribute indicated by node shape • Value of link attribute indicated by a letter

Statistics on Challenges Challenges First 100 in visualcomplexity.com C6 ~2% C5 ~10% C1 ~12% C4 ~10% C1) Basic networks C2) Node labels C3) Link labels C4) Directed networks C5) Node attributes C6) Link attributes C2 ~66%

Integrating with other methods • Social network analysis is inherently complex • Analysts must understand every node's attributes as well as relationships between nodes. • The visualizations are helpful but too messy and incomprehensible when data is huge. Statistics are used to detect important individuals, relationships, and clusters, Integrate this with Network visualization in which users can easily and dynamically filter nodes and edges. “Integrating Statistics and Visualization” by Adam Perer, Ben Shneiderman

Overview the network both statistically and visually • Present just sense of the structure, clusters and depth of a network • Present some statistics to provide a way to both confirm and quantify the visual findings

Filter and Zoom to gain deeper insights • Issues: • Panning and zooming naively is not enough • Zooming into sections of the network force users to lose the global structure. • Solution • Allow user-controlled Statistics to drive the navigation

Details on Demand • Users can select a node to see all of its attributes. • What do we achieve? – “the ability to see each node and follow its edges to all other nodes.

Lecture 12: Network Visualization