JUNG 2.0.1

JUNG 2.0.1 will prove to be a beneficial as well as reliable software that is created to provide a common and extendible language for the modeling, analysis, and visualization of data represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries.

The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation.

The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.).

JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

As an open-source library, JUNG provides a common framework for graph/network analysis and visualization. 

Enhancements:

• Bug fixes:
• • AbstractLayout.setInitializer() now prevents the user from supplying 'this' as its own initializer
  • PluggableRendererDemo: fixed bug introduced by use of NumberFormattingTransformer
  • BetwennessCentrality: removed bad println
  • MinimumSpanningForest: fixed bug in initializer with no edge weights
  • GraphMLWriter: fixed ordering problem in save()
  • OrderedKAryTree: fixed instantiation bug (internals now use List rather than an array)
  • DistanceCentrality
  • is now properly ignoring self distances when required
  • fixed assignment problem at end of getScore() method (now returns correct value on the first (uncached) invocation)
  • DirectedSparseMultigraph.findEdge(): added contains check on arguments to replace unexpected NullPointerException with a more comprehensible IllegalArgumentException
  • MinimumSpanningForest: changed use of ConstantTransformer to take a double instead of an integer to avoid a ClassCastException
  • DijkstraDistance: fixed handling of maxDistance: now allows multiple destinations/paths at max_distance (used to stop after 1) and disallows any destinations at > max_distance (used to allow exactly 1)
  • CircleLayout: fixed bug in setVertexOrder(Comparator)
  • DijkstraShortestPath: changed Transformer signature in constructors to allow subclasses of Number for edge weights
  • added Serializable tagging interface to AbstractGraph, GraphDecorator (fixes some serialization problems)
  • TreeLayout: fixed hardcoding of distances (now properly uses this.distX and this.distY where appropriate)
  • RadialTreeLayout: fixed support for modifications (esp. collapsing)
  • MinimumSpanningForest2: fixed initialization bug with null weights leading to NPE
  • DelegateTree.getPath() now returns vertices in order specified (was reverse order)
  • GraphMLReader: now correctly handles text of any length between tags
  • Lattice2DGenerator: now correctly generates undirected and directed flat and toroidal lattices (no longer adds superfluous edges)
  • WeightedChoice: fixed bug in calculating bucket weights
  • KleinbergSmallWorldGenerator: miscellaneous bug fixes (inc. use of WeightedChoice), generalized graph types that it accepts
• New features: support for rendering edge arrows in the middle of the edge (see PluggableRendererDemo for an example)
• Known issues: algorithms.scoring.BetweennessCentrality does not work properly for all weighted graphs 

Requirements:

• Universal Binary