Visual Analysis of Dynamic Networks with Geological Clustering Adel Ahmed * School of Information Technologies University of Sydney, Australia National ICT Australia, Australia Xiaoyan Fu † National ICT Australia, Australia Seok-Hee Hong ‡ School of Information Technologies University of Sydney, Australia National ICT Australia, Australia Quan Hoang Nguyen § School of Computer Sciences and Engineering University of NSW, Australia Kai Xu ¶ National ICT Australia, Australia ABSTRACT Many dynamic networks have associated geological information. Here we present two complementing visual analysis methods for such networks. The first one provides an overview with summer- ized information while the second one presents a more detailed view. The geological information is encoded in the network lay- out, which is designed to help maintain user’s mental map. We also combined visualization with social network analysis to facilitate knowledge discovery, especially to understand network changes in the context overall evolution. Both methods are applied to the “His- tory of the FIFA World Cup Competition” data set. Keywords: Network Visualization, Visual Analytics, Dynamic Network, Temporal Network, Hierarchy, Clustering, Centrality Index Terms: H.5.2 [INFORMATION INTERFACES AND PRE- SENTATION]: User Interfaces—Theory and methods; I.3.6 [Com- puter Graphics]: Methodology and Techniques—Interaction Tech- niques 1 I NTRODUCTION Many dynamic networks have geological information. One exam- ple is the email communication networks between people, in which emails can be sent from different locations such as home or of- fice. Another example is the world trading network where nodes are countries and edges are the trading between them. Inherently each country has its geological location. It is important to consider the geological information when analyzing such dynamic networks. There are several existing methods for visualization of dynamic networks [1, 3], but none of them considers the geological informa- tion. A recent relevant work by Shneiderman and Aris [6] addresses the geographical information in network visualization, but it is for static networks. Also, there are several work on combining net- work visualization with social network analysis [4, 5], but they do not consider dynamic networks and geological information. Here we propose two visual analysis methods for dynamic net- works with geological information by combining visualization with social network analysis. These two methods complement each other: The first one presents the overview of a dynamic network by showing only the summerized information, and the second one in- cludes the details of every network change. In both methods, nodes are clustered according to their geological location (i.e., the graph * e-mail: adel.ahmed@nicta.com.au † e-mail: xiaoyan.fu@nicta.com.au ‡ e-mail: shhong@it.usyd.edu.au § e-mail: quanhn@cse.unsw.edu.au ¶ e-mail: kai.xu@nicta.com.au layout considers the geological information), and the results of so- cial network analysis are mapped visually to facilitate its analysis, especially for network changes in the context of overall evolution. The two methods are applied to a real-world data set: the History of the FIFA World Cup Competition. 2 THE DATA SET AND SOCIAL NETWORK ANALYSIS The FIFA World Cup Competition History data set contains the re- sults of all the matches played in the final rounds since its found- ing in 1930. The World Cup is organized every four years, but due to the World War II, only 18 tournaments have been held so far. There are in total 79 countries that have ever joined the final rounds, and can be clustered based on their geographic locations into six football federations: AFC (Asia), CAF (Africa), CON- CACAF (North America), CONMEBOL (South America), OFC (Oceania), and UEFA (Europe). The data set can be represented as a dynamic network that consists of a series of directed graphs (one for each world cup) whose nodes are countries and edges are the matches with winning team pointing to the losing one. It is easy to see that: First, the network is dynamic, as the nodes and edges of a world cup graph changes from one year to another; second, the network is temporal, as each world cup graph has a time stamp, and thus their ordering is fixed by the time series; finally, the network has a geological clustering structure according to country location or football federation membership. Centrality index is an important concept in social network anal- ysis for analyzing the importance of actors embedded in a social network [2, 7]. In our methods, we used centrality analysis to show the strong performers over the years and their performance change over the time. We computed the centralities for each world cup and the whole data set. First, we construct a series of directed graph G i , i = 1,..., 18, one for each World Cup. Then, we construct a union graph G = G 1 ∪ G 2 ∪ ... ∪ G 18 for analyzing the global per- formance. Among the many centrality measurements available, we chose to include the results of degree centrality—defined by the number of edges incident to a node—because it is a clear indicator of team performance. Note that our methods can be easily applied to other centralities. 3 WHEEL LAYOUT In this first method, we place each country that ever joined world cup in the outermost circle of a wheel and represent each world cup as a concentric inner circle. The radius increases with the World Cup year: The circle corresponding to the first ever world cup (year 1930) is the inner most circle near the center, and the circle corre- sponding to the latest world cup (year 2006) is placed just inside the outermost circle. The performance of a country at a specific year is shown as the size of the node located at the intersection of the inner circle for that year and the radius pointed to the country node in the outermost circle. The node size is decided by its degree cen- trality value and nodes on the same World Cup circle have the same 221 IEEE Symposium on Visual Analytics Science and Technology 2007 October 30 - November 1, Sacramento, CA, USA 978-1-4244-1659-2/07/$25.00 ©2007 IEEE