JUNG, Java Universal Network/Graph Framework. JUNG — the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be 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. We hope that JUNG will make it easier for those who work with relational data to make use of one anothers’ development efforts, and thus avoid continually re-inventing the wheel.

References in zbMATH (referenced in 11 articles )

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  1. Michail, Dimitrios; Kinable, Joris; Naveh, Barak; Sichi, John V.: JGraphT -- a Java library for graph data structures and algorithms (2020)
  2. Dimitrios Michail, Joris Kinable, Barak Naveh, John V Sichi: JGraphT - A Java library for graph data structures and algorithms (2019) arXiv
  3. Lipp, Fabian; Wolff, Alexander; Zink, Johannes: Faster force-directed graph drawing with the well-separated pair decomposition (2016)
  4. Krishna, Varun; Jose, Jintomon; Suri, N. N. R. Ranga: Design and development of a web-enabled data mining system employing JEE technologies (2014) ioport
  5. Nettleton, David F.: Data mining of social networks represented as graphs (2013)
  6. Samal, Satya Swarup; Errami, Hassan; Weber, Andreas: PoCaB: a software infrastructure to explore algebraic methods for bio-chemical reaction networks (2012)
  7. Towfic, Fadi; Vanderplas, Susan; Oliver, Casey A.; Couture, Oliver; Tuggle, Christopher K.; Greenlee, M. Heather West; Honavar, Vasant: Detection of gene orthology from gene co-expression and protein interaction networks (2010) ioport
  8. Kissinger, Aleks: Exploring a quantum theory with graph rewriting and computer algebra (2009)
  9. Bistarelli, Stefano; Santini, Francesco: SCLP for trust propagation in small-world networks (2008)
  10. Kirk, Douglas; Roper, Marc; Wood, Murray: Identifying and addressing problems in object-oriented framework reuse (2007) ioport
  11. Jiang, Guofei; Chen, Haifeng; Yoshihira, Kenji: Discovering likely invariants of distributed transaction systems for autonomic system management (2006) ioport