the java universal network/graph ... -...


What’s it for?

phenomenae pertaining to relationships/links collaborations, citations/hyperlinks, email,

biological systems, friendships, telecom networks, ecological and biological systems, Markov processes, Bayes nets...

SUNBELT 2000 270 People & Structural Holes Database to UCINET to Pajek to MAGE


Programmatic Solution

ProgrammableMake it easy to do the same thing again

ExtensibleIf you need a new routine, you can put it in yourself

LibraryCan be used in a server, a client, or as part of a

larger application Open Source

Take it apart and put it together some other wayFree!


What is JUNG?

Framework for the modeling, analysis, and visualization of graphs in Java

supports most types of graphs/networks separate, flexible visualization framework rich attribute structure (metadata) network event handling predicates (subsets, constraints, filtering, ...) extensive (and growing!) library of algorithms


Dependencies

Java (1.4.2+) Commons-Collections API (3.1)

http://jakarta.apache.org/commons/collections/Predicate, set operations, wrappers, data types (BidiMap)

CERN Colt API (1.2.0)http://dsd.lbl.gov/~hoschek/colt/matrix operations, statistics

Xerces (2.6.2)http://xml.apache.org/xerces2-j/index.htmlXML parsing


Overview

Creating a graph Annotating with data Filters Graph drawing Algorithms Demo


Creating a graph

Load from a file - Pajek - GraphML (XML)

Build from scratch- Create vertices individually- Specify edges

Generate from an algorithm/model- Small-world graphs- Power-law graphs


Graph Types

AbstractArchetypeGraph

AbstractSparseGraph

ArchetypeGraph

GraphHypergraph

SparseGraph

DirectedSparseGraphUndirectedSparseGraph

UndirectedGraph DirectedGraph

HypergraphBPG

KPartiteGraph

KPartiteSparseGraph

abstract class

interface

instantiable class

KEY

A B

B implements A

A B

B extends A


Vertex Types

ArchetypeVertex

Hypervertex VertexAbstractHyperUnitBPG

AbstractSparseVertexHypervertexBPG

SimpleSparseVertex(mixed)

SimpleDirectedSparseVertex(directed)

SimpleUndirectedSparseVertex(undirected)

SparseVertex(mixed, parallel)

DirectedSparseVertex(directed, parallel)

UndirectedSparseVertex(undirected, parallel)

LeanSparseVertex(mixed, parallel)

abstract class

interface

instantiable class

KEY

A B

B implements A

A B

B extends A


Edge Types

ArchetypeEdge

EdgeHyperedge

DirectedEdge UndirectedEdge

AbstractHyperUnitBPG

AbstractSparseEdge

DirectedSparseEdge UndirectedSparseEdge

HyperedgeBPG

abstract class

interface

instantiable class

KEY

A B

B implements A

A B

B extends A


Creating a Graph

Graph g = new DirectedSparseGraph();Vertex v1 = new SparseVertex();Vertex v2 = new SparseVertex();g.addVertex( v1 );g.addVertex( v2 );Edge e = new Edge( v1, v2 );g.addEdge( e );


Adding user-defined data key-value pairs associated with

graphs, edges, vertices easy to write algorithms that operate on

relational data

String name = vertex.getUserDatum(NAME);edge.setUserDatum(DATE, new Date());


Copying and Equality

copies of vertices and edges are “equal to” the originals – equals(), hashCode()

– subgraphs– allows experimentation with mutations to

graph, without affecting original

v2 = v1.getEqualVertex(g2)


Predicates

logical expressions, e.g.v.degree() > 4e instanceof DirectedEdge

can be used to define subsets and constraints – k-partite graphs– multigraphs, simple graphs, trees, mixed-

mode graphs, hypergraphs, ...


Using graph filters

Extract subgraphs of vertices and edges– All edges with user-data “weight” > 0.2– All nodes whose “location” is “California”– All nodes within distance 3.5 of v

Chain filters together Automate complex graph

transformations


Visualizing graphs Basic layout algorithms:

– Fruchterman-Rheingold– Kamada-Kawaii– Eades– Self-Organizing Maps

Plug and play architecture- Drop in your favorite renderer, layout

Provided rendering engine is Swing-based - (but layouts are generic)


Simple Graph Display

to display an existing graph:l = new FRLayout(g);r = new PluggableRenderer();vv = new VisualizationViewer(l,r);jpanel.add(vv);

now, start customizing...

PluggableRenderer


Customizing

Layout algorithms specify vertex positionsadvancePositions()

Renderers draw vertices and edges.paintEdge( g, Edge, x1, y1, x2, y2 )paintVertex( g, Vertex, x, y )


Algorithms

clustering k-neighborhood

connected components

k-means clustering

connectivity

maximum flow

network distances

structural equivalence

centrality/importancebetweenness,markov,PageRank, hubs-and-authorities,voltage,...

bold text

show undirected edge arrows

show edge weights

edge weight highlighting

vertex degree ratio stretch

vertex voltage size

vertex degree shapes

show vertex ranks (voltages)

vertex selection stroke highlighting

vertex seed coloring

ranking

M Pazzani

M Fernandez O Mangasarian

M Hearst

M Jordan

C Lai

T Richardson

C Meek

C Glymour

P Sabes

J FrankP Cheeseman

J Stutz

M Wellman

D Cohn

A Smith

B Draper

J Erickson

C Brodley

P Smyth

J Weber

J Malik

D Heckerman

A Jones

E Knill

B Bollobas

G Das

D Gunopulos

H Mannila

S Teng

D Mitchell

R Khardon

D Roth

A Raftery

D Madigan

J Hoeting M Kersten

O Miglino

K Nafasi

C Taylor

D Wolpert

W Macready

A Hill

T Cootes

Z Ghahramani

S Dumais

P Chan

S Soatto

P Perona

R FrezzaG Picci

M Mitchell

T Jaakkola

D Haussler

P Orponen

K Murphy

H GreenspanS Belongie

R Goodman

M Holsheimer

J Matousek

L Xu

M Paterson

M KlemettinenH Toivonen

A Weigend

S Jung

J Boulicaut

S CasadeiS Mitter

J Adams

J Kosecka

R Paul

A Farquhar

R Kohavi

M Jaeger

J Ostrowski

K Bennett

A Ng

P Ronkainen

L Guibas

B Dom

M Sahami

J Bouguet

J Rice

D Dobkin

D Eppstein

T Hastie

P Agarwal

E Ukkonen

G OvertonJ Aaronson

J Haas

Z Wang

D Hart

G Gottlob

P Stolorz F Yao

A Robertson

E Horvitz

S Singh

B Fischer

T Leung

H AhonenO Heinonen

P Kilpelainen

M Atkinson

D Peel

G Miller

S Russell

W Buntine

J Fortes

L Saul

M Dillencourt

Y Weiss

C Volinsky

R Kronmal

A Gray

S Hanks

V Tsaoussidis

H Badr

D Kriegman

T Lane

J Gilbert

P Debevec

J Kivinen

J Crowcroft

R Beigel

A Mayer

B MacLennan

G Edwards

C Matheus

G Piatetsky-Shapiro

D McNeill

P Utgoff

R Fikes

R Jacobs

M Weber

J Gavrin

K Chang

D Hirschberg

E Hunt

S MacDonell

Z Galil

E Weydert

D Thomas

M Henzinger

M Burl

M Bern

P Sozou

J Graham

E DemaineM Demaine

S Spence

D Geiger

M Friedl

T Eiter

E Alpaydin

S Solloway

C Hutchinson

J WatertonD Cooper

A Barto

E Nikunen

E Dimauro

G LindenA Verkamo

I Verkamo

L Goncalves

G Cooper

A Newton

R Kraft

J Breese

N Amenta

I Cadez

C McLaren

G McLachlan

G Barequet

M Meila

Q Morris

Y Song

S Andersson

M Perlman

P Bradley

U Fayyad

D WolfG Italiano

K Tumer

E Keogh

E BernardoE Ursella

C Triggs

D Sornette

E Friedman

L DaynesT Printezis

T Pressburger

B Tuttle

J Haslam

G PageC Jackson

C Bishop

D Pavlov

M Dickerson

N de Freitas

C Stauss

R Kilgour

R Manduchi

G Graefe

R Shachter

K MosurskiR Almond

D Young

A Lapedes

R Blasi

D ChickeringR Giancarlo

K Wheeler

A Lanitis

P Prado

X Ge

S Payandeh

M Ghil

K Ide

H King

I Dryden

B Thiesson

D Pregibon

A KorholaH Olander

W Pratt

R Engelmore

A Brett

C Sayers

J Mao

M Salmenkivi

A Mamdani

J Iv

M Welling

B Kanefsky

W Taylor

C Strauss

K Walker

M Faghihi

E Di Bernardo

P Kube

K Rommelse

D Rumelhart

S GaffneyC ReinaS ChaudhuiriP Sallis

K LaaksoB Levidow

D Donnell

M Tartagni

M Vanter

N Lawrence

C FowlkesJ Roden

G Ridgeway

E Kuo

L Su

M Munich

D GolinelliG Consonni

D Cunliffe

D PsaltisG Steckman

P Courtier

M Latif

I Sim

L Cordero

L Ugarte

K Pentikousis

N Kapadia

J seck

F Lott

D Chudova

P Yiou

W Einhauser

P VetterS Goodbody

M Kawato

P Hrensen

M Levitz

clustering


Event Handling

addition/removal of vertices/edges– graphs generate events

modification of user data repository– (JUNG) objects generate events

mouse events– can be parsed as selection, panning, zooming,

pop-ups, property changes, functions, ...

objects can register as listeners


Statistical Analysis

Graph measures:– Degree distributions– Average shortest path length, diameter– Clustering coefficients

can use existing Java statistical packages CERN Colt Scientific Library Visual Numeric’s JMSL for Java


Conclusion

Provides a common language for graphs Complements rather than replaces other

graph packages/network tools A powerful framework for working with

graphs with rich attribute structure Ideally suited for building tools/

applications related to network exploration and data mining


Adoption

Users are writing social network analyses, games, animated

graph views, transportation network algorithms, Google Cartography, ...

Email visualization

Newsgroup Networks

Temporal Egocentric Communication Networks

Open-SourceCollaboration

LiveJournal networks


Integrated into an application


Freeman’s Southern Women

Shortest Path


Demo

Betweenness Centrality


Small Multiples[Sun, E1]


Thanks


Feel free to just play with it…Or join us! Active collaboration is

welcome.

Supported in part by NSF.

the java universal network/graph ... -...

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