The emergence of small world and scale free networks in a number of diverse fields has motivated lots of research in the study of complex networks. Examples include social networks, metabolic networks, computer networks, world wide web, food web, transport networks. This Ph.D. thesis introduces a visual analytics method for the analysis and mining of these complex networks. Th proposed method is used to comparatively study networks from different fields and discover similarities and dissimilarities between them. Further more, we build clustering and visualization methods based on the introduced method which leads to interesting results.