JUNG: The Java Unified Network/Graph Toolkit

Poster

Students: Danyel Fisher, Joshua O'Madadhain

Advisors: Paul Dourish, Padhraic Smyth

Abstract: JUNG is an open-source 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.

Bio: Danyel Fisher is a Ph.D. student in ICS, in the Information and Collaboration Technologies (ICT) group, working with Paul Dourish. His particular interests lie in online conversation and interaction, especially through email and shared information spaces; his research has included aspects of social network analysis and information visualization. He recieved his MS from UC Berkeley in Computer Science in 2000. He is the co-editor of the edited book "From Usenet to Cowebs: Dealing with Social Information Spaces" (Springer, 2003).

Bio: Joshua O'Madadhain is a Ph.D. student working with Padhraic Smyth in the School of Information and Computer Science at the University of California, Irvine. His interests include machine learning, data mining, information retrieval, combinatorial optimization, social network analysis, and the design of data structures and algorithms. He is currently investigating the use of machine learning techniques to create predictive models for collaboration networks. He received a M.Sc. from the Institute of Applied Mathematics at the University of British Columbia in 1999, and a joint B.A. in computer and information science and mathematics from the University of Oregon in 1994.