Visual Analysis of Overlapping Biological Networks
2009-07, Fung, David C. Y., Hong, Seok-Hee, Koschützki, Dirk, Schreiber, Falk, Xu, Kai
This paper investigates a new problem of visualizing a set of overlapping networks. We present two methods for constructing visualization of two and three overlapping networks in three dimensions. Our methods aim to achieve both drawing aesthetics (or conventions) for each individual network and exposing the common nodes between the overlapping networks. We evaluated our approaches using biological networks including protein interaction network, metabolic network, and gene regulatory network, from the bacterium Escherichia coli and crop plants to demonstrate their usefulness to support biological analysis.
2.5 D visualisation of overlapping biological networks
2008-11-10, Fung, David C. Y., Hong, Seok-Hee, Koschützki, Dirk, Schreiber, Falk, Xu, Kai
Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new problem of visualising such overlapping biological networks. Two networks overlap if they share some nodes and edges. We present an approach for constructing visualisations of two overlapping networks, based on a restricted three dimensional representation. More specifically, we use three parallel two dimensional planes placed in three dimensions to represent overlapping networks: one for each network (the top and the bottom planes) and one for the overlapping part (in the middle plane).
Our method aims to achieve both drawing aesthetics (or conventions) for each individual network, and highlighting the intersection part by them. Using three biological datasets, we evaluate our visualisation design with the aim to test whether overlapping networks can support the visual analysis of heterogeneous and yet interconnected networks.
Visual analysis of network centralities
2006, Dwyer, Tim, Hong, Seok-Hee, Koschützki, Dirk, Schreiber, Falk, Xu, Kai
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 different 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.