This article proposes a multivariate network visualization paradigm, MulNetVisBasc. Advanced Start Coordinates (ASC) are employed to place nodes on the basis of multivariate attributes and to devise an algorithm that that incorporates edge-merging and routing techniques to automatically lay-out edges; furthermore, a user-friendly human-computer interface is developed to assist users in further data analysis and mining. The experimental results suggest that the visualization of MulNetVisBasc not only uncovers the multivariate distributional characteristics of datasets intuitively, but also displays the associations of networks clearly and is helpful in discovering the implicit knowledge hidden behind datasets. The edge layout algorithm reduces the visual clutters caused by edge crossing and is suitable for relatively huge multivariate network datasets in virtue of its low complexity. Finally, the human-computer interface is flexible and convenient.