A Task Taxonomy for Temporal Graph Visualisation
Journal Article
Kerracher, N., Kennedy, J., & Chalmers, K. (2015)
A Task Taxonomy for Temporal Graph Visualisation. IEEE Transactions on Visualization and Computer Graphics, 21(10), 1160-1172. https://doi.org/10.1109/tvcg.2015.2424889
By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process invo...
Decentralized dynamic understanding of hidden relations in complex networks
Journal Article
Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018)
Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-19356-4
Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in s...
Exploring multiple trees through DAG representations
Journal Article
Graham, M., & Kennedy, J. (2007)
Exploring multiple trees through DAG representations. IEEE Transactions on Visualization and Computer Graphics, 13, 1294-1301. https://doi.org/10.1109/TVCG.2007.70556
We present a Directed Acyclic Graph visualisation designed to allow interaction with a set of multiple classification trees, specifically to find overlaps and differences betw...
A topological insight into restricted Boltzmann machines
Journal Article
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016)
A topological insight into restricted Boltzmann machines. Machine Learning, 104(2-3), 243-270. https://doi.org/10.1007/s10994-016-5570-z
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic feature...
MaTSE: the gene expression time-series explorer.
Journal Article
Craig, P., Cannon, A., Kukla, R., & Kennedy, J. (2013)
MaTSE: the gene expression time-series explorer. BMC bioinformatics, 14, https://doi.org/10.1186/1471-2105-14-S19-S1
Background
High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatmen...
Constructing and Evaluating Visualisation Task Classifications: Process and Considerations
Journal Article
Kerracher, N., & Kennedy, J. (2019)
Constructing and Evaluating Visualisation Task Classifications: Process and Considerations. Computer Graphics Forum, 36(3), 47-59. https://doi.org/10.1111/cgf.13167
Categorising tasks is a common pursuit in the visualisation research community, with a wide variety of taxonomies, typologies, design spaces, and frameworks having been develo...
Helium: visualization of large scale plant pedigrees.
Journal Article
Shaw, P., Graham, M., Kennedy, J., Milne, I., & Marshall, D. F. (2014)
Helium: visualization of large scale plant pedigrees. BMC Bioinformatics, 15, https://doi.org/10.1186/1471-2105-15-259
Plant breeders use an increasingly diverse range of data types to identify lines with desirable characteristics suitable to be taken forward in plant breeding programmes. Ther...
A survey of multiple tree visualisation.
Journal Article
Graham, M., & Kennedy, J. (2010)
A survey of multiple tree visualisation. Information Visualization, 9, 235-252. https://doi.org/10.1057/ivs.2009.29
This paper summarises the state-of-the-art in multiple tree visualisations. It discusses the spectrum of current representation techniques used on single trees, pairs of trees...
The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers
Journal Article
Stapleton, G., Chapman, P., Rodgers, P., Touloumis, A., Blake, A., & Delaney, A. (2019)
The efficacy of Euler diagrams and linear diagrams for visualizing set cardinality using proportions and numbers. PLOS ONE, 14(3), https://doi.org/10.1371/journal.pone.0211234
This paper presents the first empirical investigation that compares Euler and linear diagrams when they are used to represent set cardinality. A common approach is to use area...
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks
Journal Article
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019)
Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371
High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning,...