Many developmental, physiological, and behavioral processes depend on the 
precise expression of genes in space and time. Such spatiotemporal gene 
expression phenotypes arise from the binding of sequence-specific transcription 
factors (TFs) to DNA, and from the regulation of nearby genes that such binding 
causes. These nearby genes may themselves encode TFs, giving rise to a 
transcription factor network (TFN), wherein nodes represent TFs and directed 
edges denote regulatory interactions between TFs. Computational studies have 
linked several topological properties of TFNs - such as their degree 
distribution - with the robustness of a TFN's gene expression phenotype to 
genetic and environmental perturbation. Another important topological property 
is assortativity, which measures the tendency of nodes with similar numbers of 
edges to connect. In directed networks, assortativity comprises four distinct 
components that collectively form an assortativity signature. We know very 
little about how a TFN's assortativity signature affects the robustness of its 
gene expression phenotype to perturbation. While recent theoretical results 
suggest that increasing one specific component of a TFN's assortativity 
signature leads to increased phenotypic robustness, the biological context of 
this finding is currently limited because the assortativity signatures of 
real-world TFNs have not been characterized. It is therefore unclear whether 
these earlier theoretical findings are biologically relevant. Moreover, it is 
not known how the other three components of the assortativity signature 
contribute to the phenotypic robustness of TFNs. Here, we use publicly available 
DNaseI-seq data to measure the assortativity signatures of genome-wide TFNs in 
41 distinct human cell and tissue types. We find that all TFNs share a common 
assortativity signature and that this signature confers phenotypic robustness to 
model TFNs. Lastly, we determine the extent to which each of the four components 
of the assortativity signature contributes to this robustness.