The paper by Song, Havlin, and Makse explores the self-similarity of complex networks, challenging the common belief that these networks are not length-scale invariant. Using a renormalization procedure, they demonstrate that real complex networks, such as the World Wide Web (WWW), social networks, protein-protein interaction networks, and cellular networks, exhibit self-similar structures. This is achieved by coarse-graining the network into boxes of a given size, revealing a power-law relation between the number of boxes and the box size, indicating a finite self-similar exponent. The study also identifies critical exponents for the scale-invariant topology, providing insights into the emergence of scale-free properties in complex networks. The results suggest a common self-organization dynamics across different scales, linking statistical physics, renormalization group, fractals, and critical phenomena. The paper includes detailed analyses of various networks and comparisons with theoretical models, highlighting the significance of self-similarity in understanding complex network behavior.The paper by Song, Havlin, and Makse explores the self-similarity of complex networks, challenging the common belief that these networks are not length-scale invariant. Using a renormalization procedure, they demonstrate that real complex networks, such as the World Wide Web (WWW), social networks, protein-protein interaction networks, and cellular networks, exhibit self-similar structures. This is achieved by coarse-graining the network into boxes of a given size, revealing a power-law relation between the number of boxes and the box size, indicating a finite self-similar exponent. The study also identifies critical exponents for the scale-invariant topology, providing insights into the emergence of scale-free properties in complex networks. The results suggest a common self-organization dynamics across different scales, linking statistical physics, renormalization group, fractals, and critical phenomena. The paper includes detailed analyses of various networks and comparisons with theoretical models, highlighting the significance of self-similarity in understanding complex network behavior.