Visualisation of Temporal/Evolution Networks

Collaboration with Staffs and Students at the University of Sydney and NICTA VALACON Project memebrs.
Visualisation of various temporal/evolution networks of social networks including collaboration networks, citation networks, email networks and competition networks.



Xiaoyan Fu, Seok-Hee Hong, Nikola S. Nikolov, Xiaobin Shen, Ying Xin Wu and Kai Xu, Visualization and Analysis of Email Networks, Proceedings of APVIS (Asia-Pacific Symposium on Visualisation) 2007, IEEE, pp.1-8, 2007.

This paper presents various methods for visualization and analysis of email networks; visualization on the surface of a sphere to reveal communication patterns between different groups, a hierarchical drawing displaying the centrality analysis of nodes to emphasize important nodes, a 2.5D visualization for temporal email networks to analyze the evolution of email relationships changing over time, and an ambient display for finding social circles derived from the email network. Each method was evaluated with various data sets from a research organization. We also extended our method for visual analysis of an email virus network.

  



Adel Ahmed, Tim Dwyer, Seok-Hee Hong, Colin Murray, Le Song, Ying Xin Wu, Visualisation and Analysis of Large and Complex Scale-free Networks, Proc. of EuroVis 2005 (EUROGRAPHICS-IEEE VGTC Symposium on Visualization), pp. 1-8, IEEE, 2005.

Scale-free networks appear in many application domains such as social and biological networks [BA99, BB03, BO04]. Roughly speaking, scale-free networks have power-law degree distribution, ultra-short average path length and high clustering coefficient. This paper presents new methods for visualising scale-free networks in three dimensions. To make effective use of the third dimension and minimise occlusion, we produce graph visulaisations with nodes constrained to lie on parallel planes or on the surface of spheres. We implement the algorithms using a variation of a fast force-directed graph layout method. Results with real world data sets such as IEEE InfoVis citation and collaboration networks and a protein-protein interaction network show that our method can be useful for visual analysis of large and complex scale-free networks. We also discuss the issue of visualisation of evolving networks and network integration



A. Ahmed, X. Fu, S. Hong, Q. Nguyen and K. Xu, "Visual Analysis of History of World Cup: A Dynamic Network with Dynamic Hierarchy and Geographic Clustering", Visual Information Communication (Proceedings of VINCI'09), Springer, pp. 25-39, 2010.

In this paper, we present new visual analysis methods for history of the FIFA World Cup competition data, a social network from Graph Drawing 2006 Competition. Our methods are based on the use of network analysis method, and new visualization methods for dynamic graphs with dynamic hierarchy and geographic clustering.
More specifically, we derive a dynamic network with geographic clustering from the history of the FIFA World Cup competition data, based on who-beats-whom relationship. Combined with the centrality analysis (which defines dynamic hierarchy) and the use of the union of graphs (which determines the overall layout topology), we present three new visualization methods for dynamic graphs with dynamic hierarchy and geographic clustering: wheel layout, radial layout and hierarchical layout.
Our experimental results show that our visual analysis methods can clearly reveal the overall winner of the World Cup competition history as well as the strong and weak countries. Further, one can analyze and compare the performance of each country for each year along the context with their overall performance. This enables to confirm the expected and discover the unexpected.

    
    



Video

World Cup Movie1

World Cup Movie2