information visualization for social network analysis,

Information Visualization for

Social Network Analysis

Ben Shneiderman [email protected]

Twitter: @benbendc

Founding Director (1983-2000), Human-Computer Interaction Lab

Professor, Department of Computer Science Member, Institute for Advanced Computer Studies

Interdisciplinary research community

- Computer Science & Info Studies

- Psych, Socio, Poli Sci & MITH

(www.cs.umd.edu/hcil)

Design Issues

• Input devices & strategies

• Keyboards, pointing devices, voice

• Direct manipulation

• Menus, forms, commands

• Output devices & formats

• Screens, windows, color, sound

• Text, tables, graphics

• Instructions, messages, help

• Collaboration & Social Media

• Help, tutorials, training

• Search

www.awl.com/DTUI

Fifth Edition: 2010

• Visualization

HCI Pride: Serving 5B Users

Mobile, desktop, web, cloud

Diverse users: novice/expert, young/old, literate/illiterate,

abled/disabled, cultural, ethnic & linguistic diversity, gender,

personality, skills, motivation, ...

Diverse applications: E-commerce, law, health/wellness,

education, creative arts, community relationships, politics,

IT4ID, policy negotiation, mediation, peace studies, ...

Diverse interfaces: Ubiquitous, pervasive, embedded, tangible,

invisible, multimodal, immersive/augmented/virtual, ambient,

social, affective, empathic, persuasive, ...

Using Vision to Think

• Visual bandwidth is enormous

• Human perceptual skills are remarkable

• Trend, cluster, gap, outlier...

• Color, size, shape, proximity...

• Human image storage is fast and vast

• Opportunities

• Spatial layouts & coordination

• Information visualization

• Scientific visualization & simulation

• Telepresence & augmented reality

• Virtual environments

Spotfire: DC natality data

Information Visualization: Mantra

• Overview, zoom & filter, details-on-demand










Information Visualization: Data Types

• 1-D Linear Document Lens, SeeSoft, Info Mural

• 2-D Map GIS, ArcView, PageMaker, Medical imagery

• 3-D World CAD, Medical, Molecules, Architecture

• Multi-Var Spotfire, Tableau, GGobi, TableLens, ParCoords,

• Temporal LifeLines, TimeSearcher, Palantir, DataMontage

• Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap

• Network Pajek, JUNG, UCINet, SocialAction, NodeXL

Info

Viz

Sci

Viz

.

Jenny Preece (PI), Peter Pirolli & Ben Shneiderman (Co-PIs) www.tmsp.umd.edu

NSF Workshops: Academics, Industry, Gov’t

- Scientific Foundations

- Advancing Design of

Social Participation Systems

- Visions of What is Possible With Sharable

Socio-technical Infrastructure

- Participating in Health 2.0

- Educational Priorities for

Technology Mediated Social Participation

- Engaging the Public in Open Government:

Social Media Technology and

Policy for Government Transparency

Cyberinfrastructure: Social Action on National Priorities

Summer Social Webshop: August 23-26, 2011

UN Millennium Development Goals

• Eradicate extreme poverty and hunger

• Achieve universal primary education

• Promote gender equality and empower women

• Reduce child mortality

• Improve maternal health

• Combat HIV/AIDS, malaria and other diseases

• Ensure environmental sustainability

• Develop a global partnership for development

To be achieved by 2015

State-of-the-art network visualization

Node Placement Methods

• Node-link diagrams

• Force-directed layout

• Geographical map

• Circular layout

• Temporal layout

• Clustering

• Layouts based on node attributes

• Matrix-based

• Tabular textual

NetViz Nirvana

1) Every node is visible

2) For every node

you can count its degree

3) For every link

you can follow it

from source to destination

4) Clusters and outliers are identifiable

• Group nodes into regions According to an attribute

Categorical, ordinal, or binned numerical

• In each region: Place nodes according to other attribute(s)

• Give users control of link visibility

1) NVSS: Semantic Substrates

Force Directed Layout

36 Supreme & 13 Circuit Court decisions

268 Citations on Regulatory Takings 1978-2002

Network Visualization by Semantic Substrates

NVSS 1.0

Filtering links by source-target

Filtering links by time attribute (1)

• Meaningful

layout of nodes

• User controlled

visibility of links

• Cross refs in

11 Circuit Courts

(green) + few refs to

District Court cases

www.cs.umd.edu/hcil/nvss


NVSS 2.0

with Substrate Designer


Senate 2007: 180 out of 310 Votes in Common

2) SocialAction:

Integrating Statistics & Visualization

290 out of 310 Votes in Common

Social Action: 2007 Senate Votes

NodeXL: Network Overview for Discovery & Exploration in Excel

www.codeplex.com/nodexl

NodeXL: Import Dialogs

www.codeplex.com/nodexl

Tweets at #WIN09 Conference: 2 groups

Twitter discussion of #GOP

Red: Republicans, anti-Obama,

mention Fox

Blue: Democrats, pro-Obama,

mention CNN

Green: non-affiliated

Node size is number of followers

Politico is major bridging group

CHI2010 Twitter Community

www.codeplex.com/nodexl/

Flickr networks

Flickr clusters for “mouse”

Computer Mickey

Animal

Figure 7.11. : Lobbying Coalition Network connecting organizations (vertices) that have jointly filed

comments on US Federal Communications Commission policies (edges). Vertex Size represents

number of filings and color represents Eigenvector Centrality (pink = higher). Darker edges connect

organizations with many joint filings. Vertices were originally positioned using Fruchterman-

Rheingold and hand-positioned to respect clusters identified by NodeXL’s Find Clusters algorithm.

WWW2010 Twitter Community

WWW2011 Twitter Community: Grouped

Analogy: Clusters Are Occluded Hard to count nodes, clusters

Separate Clusters Are More Comprehensible

Twitter Network for “msrtf11 OR techfest ”

US Senate Co-Voting Network 2007

South

Midwest

Northeast

Mountain

Paci

fic

US Senate Co-Voting Network 2007, Clustered

Small-World Graph with 5 Clusters

Pseudo-Random Graph with 5 Clusters

Scale-free Network with 10 Clusters

Innovation Patterns: 11,000 vertices, 26,000 edges

Patent

Tech

SBIR (federal)

PA DCED (state)

Related patent

2: Federal agency

3: Enterprise

5: Inventors

9: Universities

10: PA DCED

11/12: Phil/Pitt metro cnty

13-15: Semi-rural/rural cnty

17: Foreign countries

19: Other states

Pittsburgh Metro

Westinghouse Electric

Pharmaceutical/Medical

No Location Philadelphia

Navy

Patent

Tech

SBIR (federal)

PA DCED (state)

Related patent

2: Federal agency

3: Enterprise

5: Inventors

9: Universities

10: PA DCED

11/12: Phil/Pitt metro cnty

13-15: Semi-rural/rural cnty

17: Foreign countries

19: Other states

Pittsburgh Metro

Westinghouse Electric

Pharmaceutical/Medical

No Location Philadelphia

Navy

Innovation Clusters: People, Locations, Companies

Discussion Group Postings, color by topic

www.cs.umd.edu/hcil/non

nationofneighbors.net

Analyzing Social Media Networks with NodeXL

I. Getting Started with Analyzing Social Media Networks

1. Introduction to Social Media and Social Networks

2. Social media: New Technologies of Collaboration

3. Social Network Analysis

II. NodeXL Tutorial: Learning by Doing

4. Layout, Visual Design & Labeling

5. Calculating & Visualizing Network Metrics

6. Preparing Data & Filtering

7. Clustering &Grouping

III Social Media Network Analysis Case Studies

8. Email

9. Threaded Networks

10. Twitter

11. Facebook

12. WWW

13. Flickr

14. YouTube

15. Wiki Networks

http://www.elsevier.com/wps/find/bookdescription.cws_home/723354/description

Social Media Research Foundation

Social Media Research Foundation smrfoundation.org

We are a group of researchers who want to create

open tools, generate and host open data, and

support open scholarship related to social media.

smrfoundation.org

29th Annual Symposium

May 22-23, 2012

www.cs.umd.edu/hcil

information visualization for social network analysis,

Technology

demand overview

node attributes matrix

search visualization

mantra overview

social action

collaboration social

social media technology

semantic substrates