The high relevance between functional organization and topological features has motivated the development of statistical measures to characterize cellular networks. Increasingly, these measures reveal that biological network organization is characterized by the power law of degree distribution, the concept of modularity and the degree correlations on connected nodes [

1-

3]. Networks with high modularity have dense connections between the nodes within same cellular functions but sparse connections between nodes in different functions. Furthermore, a central theory in biology is the hierarchical organization of cellular processes, which means that high-level processes are build by connecting low-level ones [

4,

5]. For example, the process mitosis is composed by several low-level functions, such as spindle assembly, centrosome separation and chromosome alignment. Consequently, it is reasonable to suppose that functional modules of interest are hierarchically organized in the same way, that small modules are combined into larger modules and then further combined into even larger ones. This complexity, therefore, poses great challenges to researchers trying to understand the modularity structure of cellular networks.

To identify the hierarchical modularity of metabolic networks, Ravasz et al. focused on detecting a “global signature” of network architecture [

6,

7]. In Ravasz’s study, they revealed that for metabolic networks and for certain hierarchical networks the clustering coefficient,

*C*(

*k*), of a node follows a scaling law with degree

*kC*(

*k*)

~

*k*^{-1}. To explain this, they proposed a network model which possesses both the power law of degree distribution and the scaling law of

*C*(

*k*). The starting point of this network model is a small cluster of five fully connected nodes; then creates four identical replicas, connecting the peripheral nodes of each cluster to the central node of the old cluster, resulted in a large 25-node cluster. Next, four replicas of this 25-node cluster are generated and the 16 peripheral nodes are connected to the central node of the old cluster, obtaining a larger cluster of 125 nodes. These replication and connection steps can be repeated indefinitely to generate a hierarchical architecture. In each step

*i*, the number of nodes in the network is 5

^{i}. This network model, which we explicitly denote by “deterministic hierarchical model”, has subsequently a great influence on the studies of network biology [

8,

9], and the scaling of

*C*(

*k*) is widely used to identify whether or not a network is hierarchically organized nowadays.

Two former studies have suggested that the decrease of

*C*(

*k*) can be tentatively attributed to the tendency that large degree nodes are connected to small degree ones in biological networks[

1,

10]. For example, Soffer and Vazquez proposed a novel measurement of clustering coefficient taking into account of the neighborhood degree of node, which didn’t scale with

*k*. Their work suggested that the variation of

*C*(

*k*) can be attributed to neighborhood degree distribution. However, the “deterministic model” is also anti-correlated. Thus, it is still possible that both the degree anti-correlation and the variation of

*C*(

*k*) is the reflection of hierarchy, suggesting that proper “null model” is needed to clarify their relationships. Moreover, metabolic networks is nicely approximated by

*C*(

*k*)

~

*k*^{-1}, providing a strong evidence for the existence of hierarchy in these networks. However, to our best knowledge, former studies didn’t directly indicated why

*C*(

*k*) strictly follows this scaling law (

*k*^{-1}) in metabolic networks. These may be the reasons why the variation of

*C*(

*k*) is still widely used in assessing biological network hierarchy. In fact, almost every study on biological networks that observed the variation of

*C*(

*k*), including protein-protein networks, functional networks, human disease networks or even ecological networks, claimed that they have found a hierarchical modular structure, for example [

11-

17]. This situation suggested that, a further and systematical investigation of clustering coefficient focused on different types of biological networks is necessary. In this work we revealed the reason why

*C*(

*k*) scales with

*k*^{-1} in metabolic networks and suggested by “null model” that the variation of

*C*(

*k*) is neither sufficient nor exclusive for a hierarchical network. Our findings suggest the existence of spoke-like topology as opposed to “deterministic hierarchical model”.