N$_{B}$ ( ℓ ) ∼ ℓ $_{−}$d$_{B}$ , (3) 6 time evolution (Fig. 3a). This value reveals a considerable degree of modularity in the entire system (for comparison, a random network has d$_{M}$ = 0 and a uniform lattice has d$_{M}$ = 1 [30]), as evidenced by the network structure in Fig. 1. The lack of a characteristic length-scale in the modularity shown in Fig. 3a suggests that the modules appear at all length-scales, i.e. modules are organized within larger modules in a self-similar way, so that the inter-connections between those clusters repeat the basic modular character of the entire NHCD. Thus, the NHCD remains statistically invariant when observed at different scales. Varying the module size ℓ yields the scaling relation for the number of modules (Fig. 3b): where d$_{B}$ represents the fractal dimension of the network [13]. We find that the fractal character of the modules is established at the early stages, yielding d$_{B}$ ≃ 1 . 4 as early as 30 days (Fig. 3b). As the network evolves, the fractal dimension increases slightly and finally reaches d$_{B}$ ≃ 1 . 9. The significance of Eq. (2) is that the modules need to be interpreted at a given length- scale. Figure 2b shows an example of such hierarchical organization [Fig. 2c and SI-Fig. 6 show the full modular structure, while a list of detected modules appears in the SI Appendix]. Three types of communities of cell types are clearly identified in Fig. 2b as we change ℓ . (i) The known functional modules: The entire eye lineage [1, 5, 6, 14] is detected as a single module by the box-covering algorithm at ℓ = 11, while the entire neural lineage [1– 3, 5, 6, 14, 15] appears at ℓ = 15. Finer and coarser novel modules are identified by the algorithm. (ii) Sub-modules: At ℓ = 11 the neural lineage is split into the main neural and the supporting glial cell modules, while for ℓ = 7 sub-modules are identified in the eye system. (iii) Super-modules: When we increase the length to ℓ = 19, the eye and neural system form a single super-module. Thus, each cell type is connected to other types according to which groups of nodes of all sizes self-organize following a single principle. This property allows us to renormalize the network [13] by replacing each detected module by a single supernode to identify the network of modules as shown in Fig. 2c. Following the evolution and inter-dependence of these super-modules, as seen in Fig. 2c, identifies families of cell types at varying scales. This modularity map is useful in proposing future research ties between previously unrelated domains in organ functions. The dynamics leading to such a structure can be unraveled by the study of the NHCD