Dependency network

This approach provides a new system level analysis of the activity and topology of directed networks. The approach extracts causal topological relations between the networks nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity).

In the case of network activity, the analysis is based on partial correlations, which are becoming ever more widely used to investigate complex systems. In simple words, the partial (or residual) correlation is a measure of the effect (or contribution) of a given node, say j, on the correlations between another pair of nodes, say i and k. Using this concept, the dependency of one node on another node, is calculated for the entire network. This results in a directed weighted adjacency matrix, of a fully connected network. Once the adjacency matrix has been constructed, different algorithms can be used to construct the network, such as a threshold network, Minimal spanning tree(MST), Planar Maximally Filtered Graph (PMFG), and others.

The partial correlation based Dependency Networks is a revolutionary new class of correlation based networks, which is capable of uncovering hidden relationships between the nodes of the network.

References / Links

• Wikipedia article on Dependency Network- [1]