An important issue in network visualization is the problem of obtaining a good layout for a network. For a given network, which may be either weighted or unweighted, the problem is to position the nodes in the network in a two-dimensional space in such a way that an attractive layout is obtained. Many layout techniques have been proposed . In the visualization of bibliometric networks, multidimensional scaling and the layout technique of Kamada and Kawai  have for instance been used a lot. More recently, the VOS (visualization of similarities) layout technique , implemented in our VOSviewer software (www.vosviewer.com) , is often used for bibliometric network visualization. There is no layout technique that is generally considered to give optimal results. One reason for this is that comparisons between layouts produced by different techniques involve a lot of subjectiveness. Someone may consider one layout to be more attractive than another, but someone else may have an opposite opinion on this. In addition, the attractiveness of a layout may depend on the type of visualization that is needed. For instance, some layouts may be more attractive for interactive visualizations (e.g., in a software tool with zooming functionality), while other layouts may be more attractive for static visualizations. Furthermore, different types of networks may benefit from different layout techniques. In recent studies [5, 6], the idea of parameterized layout techniques has been introduced. Parameterized layout techniques produce different types of layouts depending on the values chosen for their parameters. In this research, we present a comprehensive study of a parameterized version of our VOS layout technique. Two parameters are included. Like in , these are referred to as attraction and repulsion parameters. We compare the layouts obtained for different parameter values. Comparisons are made both subjectively using the VOSviewer software (i.e., which layout do we find most appealing?) and more objectively using so-called meta-criteria [6, 7]. Sensitivity to local optima is taken into account as well. Comparisons are made for all important types of bibliometric networks, in particular co-authorship, citation, co-citation, bibliographic coupling, and co-occurrence networks. Both smaller and larger networks are considered.