Recent technological advances have led to big complex data models in many domains, including social networks and biological networks. Good visualisation can reveal the hidden structure of the networks and amplifies human understanding, thus leading to new insights and findings. However, visualisation of big complex networks is challenging due to scalability and complexity.This talk will introduce a new framework for sublinear-time force computation for visualization of big complex graphs, based on the vertex sampling for repulsion force computation and edge sparsification for attraction force computation.Experiments show that our new sublinear-time force computation algorithms run much faster than existing linear-time force computation methods, while surprisingly achieving significant improvement in quality metrics such as shape‐based and edge crossing metrics.

Seokhee Hong is a professor at the University of Sydney. She was ARC Future Fellow, Humboldt Fellow, ARC Research Fellow, and a project leader of VALACON (Visualisation and Analysis of Large and Complex Networks) project at NICTA (National ICT Australia).Her research interests include Graph Drawing, Algorithms, Information Visualisation and Visual Analytics. She serves as a Steering Committee member of IEEE PacificVis (International Symposium on Pacific Visualisation) and ISAAC (International Symposium on Algorithms and Computations), and an editor of JGAA (Journal of Graph Algorithms and Applications).