Visualizing networks using adjacency matrices: Progresses and challenges

Visualizing networks has become a very important research and application topic in the recent years, due to the availability of network data through the web, but also to the need of analyzing several types of networks such as computer networks, social networks, biological networks (e.g. gene similarities or biological pathways). Until 2000, the node-link diagram was the only representation used. However, this representation suffers from many readability issues when the network becomes dense. In 2003, we showed that the adjacency matrix representation was more effective to visualize networks when they were dense. In this talk, we will present new methods designed by our group and others to make adjacency matrices usable for network analysis and exploration. We will explore various alternatives around the matrix representations (combined views, augmented matrices, hybrid node-link and matrix views), interaction and navigation methods for very large networks and algorithmic methods to reorder the matrices in order to show high-level structures. We will conclude with some research challenges ahead in matrix-based visualization of networks.