A Fast Algorithm for Balanced Graph Clustering

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dc.contributor.author Huang, M
dc.contributor.author Nguyen, Q
dc.contributor.editor Banissi
dc.contributor.editor E
dc.contributor.editor Burkhard
dc.contributor.editor A, R
dc.contributor.editor Grinstein
dc.contributor.editor G
dc.contributor.editor al, E
dc.date.accessioned 2009-11-09T05:35:39Z
dc.date.issued 2007-01
dc.identifier.citation Information Visualization, 2007, pp. 46 - 52
dc.identifier.isbn 0-7695-2900-3
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/2425
dc.description.abstract Scalability problem is a long-lasting challenge for both information visualization and graph drawing communities. Available graph visualization techniques could perform well for small or medium size graphs but they are rarely able to handle very large and complex graphs. One of effective approach to solve this problem is to employ graph abstraction; that is to hierarchically partitioning the complete graph into a clustered graph. A graph visualization technique is then applied to display the abstract view of this clustered graph with partially displayed detail of one or a few sub-graphs where the user is currently focusing on. This reduces the complexity of display and makes it easier for users to interpret, perceive and navigate the large scale information. In this paper, we propose a graph clustering method which can quickly discover the community structure embedded in large graphs and partition the graph into densely connected sub-graphs. The proposed algorithm can not only run fast, but also achieve a consistent partitioning result in which a graph is divided into a set of clusters of the similar size in terms of their visual complexity and the number of nodes and edges. In addition, we also provide a mechanism to partition very dense graphs in which the number of edges is much larger than the number of nodes.
dc.publisher IEEE Computer Society Publisher
dc.relation.isbasedon 10.1109/IV.2007.10
dc.title A Fast Algorithm for Balanced Graph Clustering
dc.type Conference Proceeding
dc.parent Information Visualization
dc.journal.number en_US
dc.publocation Los Alamitos, USA en_US
dc.publocation Los Alamitos, USA
dc.identifier.startpage 46 en_US
dc.identifier.endpage 52 en_US
dc.cauo.name FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.conference International Conference on Information Visualisation
dc.conference.location Zurich, Switzerland en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 024665
dc.personcode 990771
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.custom International Conference on Information Visualisation en_US
dc.date.activity 20070704 en_US
dc.date.activity 2006-02-28
dc.date.activity 2007-07-04
dc.location.activity Zurich, Switzerland en_US
dc.description.keywords visual communication, creativ eprocess, advertising, design, animation,, digital media
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history School of Software (ID: 337)
utslib.collection.history Closed (ID: 3)

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