Back to publications

Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation

Molina-Solana, Miguel and Birch, David and Guo, Yi-ke
Applied Soft Computing , 53, pp. 227–235 (2017)

Abstract:

This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

Links:

DOI: 10.1016/j.asoc.2016.12.044
PDF: https://www.doc.ic.ac.uk/ mmolinas/publications/molina-asoc17.pdf

Bibtex:

@article{Molina-Solana2017b,
  author = {Molina-Solana, Miguel and Birch, David and Guo, Yi-ke},
  title = {Improving Data Exploration in Graphs with Fuzzy Logic and Large-Scale Visualisation},
  journal = {Applied Soft Computing},
  year = {2017},
  volume = {53},
  pages = {227--235},
  comment = {https://www.doc.ic.ac.uk/~mmolinas/publications/molina-asoc17.pdf},
  doi = {10.1016/j.asoc.2016.12.044},
  timestamp = {12}
}