Centrality-Based Visualisation

Collaboration with Staffs and Students at the University of Sydney and NICTA VALACON Project memebrs.


Centrality-Based Network Comparison


Tim Dwyer, Seok-Hee Hong, Dirk Koschuetzki, Falk Schreiber and Kai Xu, Visual Analysis of Network Centralities, Proceedings of Asia Pacific Symposium on Information Visualisation (APVIS 2006), pp. 189-197, 2006.

Centrality analysis determines the importance of vertices in a network based on their connectivity within the network structure. It is a widely used technique to analyse network-structured data. A particularly important task is the comparison of different centrality measures within one network. We present three methods for the exploration and comparison of centrality measures within a network: 3D parallel coordinates orbit-based comparison and hierarchy-based comparison. There is a common underlying idea to all three methods: for each centrality measure the graph is copied and drawn in a separate 2D plane with vertex position dependent on centrality. These planes are then stacked into the third dimension so that the different centrality measures may be easily compared. Only the details of how centrality is mapped to vertex position are dierent in each method. For 3D parallel coordinates vertices are placed on vertical lines; for orbit-based comparison vertices are placed on concentric circles and for hierarchy-based comparison vertices are placed on horizontal lines. The second and third solutions make it particularly easy to track changing vertex-centrality values in the context of the underlying network structure. The usability of these methods is demonstrated on biological and social networks.

     



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.

     



Adel Ahmed, Tim Dwyer, Seok-Hee Hong, Colin Murray, Le Song, Ying Xin Wu: Visualisation and Analysis of Large and Complex Scale-free Networks. EuroVis 2005: 239-246

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 [BA99, BB03, BO04]. 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 [QE00]. 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.

     



Centrality-Based Planarisation and Thickness

     


Centrality-Based Email Network Visualisation

     


Centrality-Based Social Network Visualisation