### Abstract

Much effort has been devoted to assess the importance of nodes in complex biological networks (such as gene transcriptional regulatory networks,protein interaction networks, and neural networks). Examples of commonly used measures of node importance include node degree, node centrality, and node vulnerability score (the effect of the node deletion on the network efficiency). Here, we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. To this end, we first define the dependency of node *i* on node *j* (or the influence of node *j* on node *i*), *D*(*i*, *j*) as the average over all nodes *k* of the difference between the *i* − *k* correlation and the partial correlations between these nodes with respect to node *j*. Note that the dependencies, *D*(*i*, *j*) define a directed weighted matrix, since, in general, *D*(*i*, *j*) differs from *D*( *j*, *i*). For this reason, many of the commonly used measures of node importance, such as node centrality, cannot be used. Hence, to assess the node importance of the dependency networks, we define the system level influence (SLI) of antigen *j*, *SLI*( *j*) as the sum of the influence of *j* on all other antigens *i*. Next, we define the system level influence or the influence score of antigen *j*, *SLI*( *j*) as the sum of *D*(*i*, *j*) over all nodes *i*. We introduce the new approach and demonstrate that it can unveil important biological information in the context of the immune system. More specifically, we investigated antigen dependency networks computed from antigen microarray data of autoantibody reactivity of IgM and IgG isotypes present in the sera of ten mothers and their newborns. We found that the analysis was able to unveil that there is only a subset of antigens that have high influence scores (SLI) common both to the mothers and newborns. Networks comparison in terms of modularity (using the Newman’s algorithm) and of topology (measured by the divergence rate) revealed that, at birth, the IgG networks exhibit a more profound global reorganization while the IgM networks exhibit a more profound local reorganization. During immune system development, the modularity of the IgG network increases and becomes comparable to that of the IgM networks at adulthood. We also found the existence of several conserved IgG and IgM network motifs between the maternal and newborns networks, which might retain networkinformation as our immune system develops. If correct, these findings provide a convincing demonstration of the effectiveness of the new approach to unveil most significant biological information. Whereas we have introduced the new approach within the context of the immune system, it is expected to be effective in the studies of other complex biological social, financial, and manmade networks.

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