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JUNG: the Java Universal Networks/Graph APIDanyel Fisher and Joshua O'Madadhain

http://jung.sourceforge.net{danyelf, jmadden}@ics.uci.edu

open source, available under a BSD licensemake networks a component of any projectjoin an active user and developer community

run common network analyses write your own algorithms and toolsincorporate existing 3rd party libraries and codeautomate complex analyses

annotate vertices, edges, and graphs with arbitrary dataread and write common file formatsbuild graphs, trees, hypergraphs, k-partite graphs with directed and undirected, parallel, weighted edges

build interactive toolseasily configurable rendererlayouts: spring-embedder, Kamada-Kawaii, or roll your own

A visualization of the LiveJournal "friends" network. Pink sample nodes are sized by the ratio of in-degree to out-degree.(Courtesy S. Abrams)

Various shortest paths (in blue and red) on a randomly-generated unweighted, undirected graph (in gray).

Part of an email network from one user's perspective. Edges between names (gray rectangles) represent carbon-copied messages. Both nodes and edges are colored by the most recent contact; lighter is more recent.

Danyel Fisher is an Informatics PhD candidate with an interest in computer-supported collaborative work; Joshua O'Madadhain is a PhD student in Artificial Intelligence with an interest in machine learning.

Both use social network analysis in their research.

JUNG was co-created with Scott White and Yan-Biao Boey.

JUNG is supported in part by the National Science Foundation under Grant Nos. IIS-0083489 and IIS-0133749 and by the Knowledge Discovery and Dissemination (KD-D) Program.

structural equivalence n PageRank n betweenness clustering n shortest path n centrality measures n max flow n HITS n small world n power law n scale-free n graph folding

A screenshot from the KDD Netsight tool shows co-publication links around a central node (in blue). Larger nodes indicate greater PageRank.

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Graph loaded: 286 vertices / 554 edges

The well-known "Southern Women" dataset shows a bipartite graph of (blue) parties and (red) women who attended them.

On the left is the bipartite graph. On the right is a graph that connects women who attended the same parties. Note that all women are connected to each other.

By interactively removing some parties (x), we see a very different connection graph: two tight clusters.

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