The paper by Clauset, Moore, and Newman explores the hierarchical structure of networks and its implications for understanding and predicting missing links. Networks often exhibit hierarchical organization, where vertices are divided into groups that further subdivide into smaller groups, and so on. This hierarchical structure can explain various topological properties of networks, such as degree distributions, clustering coefficients, and short path lengths. The authors propose a method to infer this hierarchical structure from network data using a hierarchical random graph model, which allows for the generation of artificial networks with specified hierarchical structures. They demonstrate that this model can accurately predict missing connections in partially known networks, outperforming other methods in terms of accuracy and applicability to a wider range of network structures. The hierarchical decomposition also provides a clear and concise summary of the network's structure through a consensus dendrogram. The method is validated using three example networks from different fields, showing that it can effectively predict missing interactions and reduce the effort required to establish network topology.The paper by Clauset, Moore, and Newman explores the hierarchical structure of networks and its implications for understanding and predicting missing links. Networks often exhibit hierarchical organization, where vertices are divided into groups that further subdivide into smaller groups, and so on. This hierarchical structure can explain various topological properties of networks, such as degree distributions, clustering coefficients, and short path lengths. The authors propose a method to infer this hierarchical structure from network data using a hierarchical random graph model, which allows for the generation of artificial networks with specified hierarchical structures. They demonstrate that this model can accurately predict missing connections in partially known networks, outperforming other methods in terms of accuracy and applicability to a wider range of network structures. The hierarchical decomposition also provides a clear and concise summary of the network's structure through a consensus dendrogram. The method is validated using three example networks from different fields, showing that it can effectively predict missing interactions and reduce the effort required to establish network topology.