Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Structural differences between two graphs through hierarchies
Proceedings of Graphics Interface 2009
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Dynamic network data exploration through semi-supervised functional embedding
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Graph visualization with latent variable models
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Proceedings of Graphics Interface 2010
Towards open ontology learning and filtering
Information Systems
TVi: a visual querying system for network monitoring and anomaly detection
Proceedings of the 8th International Symposium on Visualization for Cyber Security
The visualization of mass information in social network with a holistic view
EVA'11 Proceedings of the 2011 international conference on Electronic Visualisation and the Arts
Alternate views of graph clusterings based on thresholds: a case study for a student forum
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
replay: visualising the structure and behaviour of interconnected systems
ACSC '13 Proceedings of the Thirty-Sixth Australasian Computer Science Conference - Volume 135
A GPU-based method for computing eigenvector centrality of gene-expression networks
AusPDC '13 Proceedings of the Eleventh Australasian Symposium on Parallel and Distributed Computing - Volume 140
Visual analysis of large-scale network anomalies
IBM Journal of Research and Development
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This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network’s underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.