This paper presents a unified approach to mapping and clustering of bibliometric networks. The authors propose that both the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can be derived from the same underlying principle. They demonstrate this by applying their approach to the most frequently cited publications in the field of information science between 1999 and 2008. The paper discusses the complementary nature of mapping and clustering techniques in bibliometric analysis. Mapping provides a visual representation of network structure, while clustering identifies groups of nodes with similar characteristics. However, these techniques are often used separately, leading to inconsistencies in results. The authors argue that a unified approach, based on similar principles, enhances the transparency of the analysis and avoids unnecessary complexity. The unified approach is based on minimizing a function that combines the association strength between nodes and their spatial arrangement. This function is used to derive both mapping and clustering techniques. In the case of mapping, it is shown to be equivalent to the VOS mapping technique, which is closely related to multidimensional scaling. In the case of clustering, it is shown to be equivalent to a weighted and parameterized variant of modularity-based clustering, which is a popular method in physics and network science. The authors illustrate their approach by producing a combined mapping and clustering of the most frequently cited publications in information science. The results show a clear distinction between the information seeking and retrieval (ISR) subfield and the informetrics subfield. The clustering reveals 25 clusters, with the largest cluster containing 123 publications focused on citation analysis. The paper concludes that a unified approach to mapping and clustering can be highly valuable in bibliometric analysis. It ensures that the techniques used are based on similar principles, leading to more consistent results. The authors also highlight the practical benefits of this approach, particularly in science policy contexts where detailed and general maps are needed. The unified approach is implemented in the VOSviewer software, which is freely available for use.This paper presents a unified approach to mapping and clustering of bibliometric networks. The authors propose that both the VOS mapping technique and a weighted and parameterized variant of modularity-based clustering can be derived from the same underlying principle. They demonstrate this by applying their approach to the most frequently cited publications in the field of information science between 1999 and 2008. The paper discusses the complementary nature of mapping and clustering techniques in bibliometric analysis. Mapping provides a visual representation of network structure, while clustering identifies groups of nodes with similar characteristics. However, these techniques are often used separately, leading to inconsistencies in results. The authors argue that a unified approach, based on similar principles, enhances the transparency of the analysis and avoids unnecessary complexity. The unified approach is based on minimizing a function that combines the association strength between nodes and their spatial arrangement. This function is used to derive both mapping and clustering techniques. In the case of mapping, it is shown to be equivalent to the VOS mapping technique, which is closely related to multidimensional scaling. In the case of clustering, it is shown to be equivalent to a weighted and parameterized variant of modularity-based clustering, which is a popular method in physics and network science. The authors illustrate their approach by producing a combined mapping and clustering of the most frequently cited publications in information science. The results show a clear distinction between the information seeking and retrieval (ISR) subfield and the informetrics subfield. The clustering reveals 25 clusters, with the largest cluster containing 123 publications focused on citation analysis. The paper concludes that a unified approach to mapping and clustering can be highly valuable in bibliometric analysis. It ensures that the techniques used are based on similar principles, leading to more consistent results. The authors also highlight the practical benefits of this approach, particularly in science policy contexts where detailed and general maps are needed. The unified approach is implemented in the VOSviewer software, which is freely available for use.