Analysing structure in complex networks using quality functions evolved by genetic programming

When studying complex networks, we are often interested in identifying structures within the networks. Previous work has successfully used algorithmically identified network structures to predict functional groups; for example, where structures extracted from protein-protein interaction networks have been predictive of functional protein complexes. One way structures in complex networks have previously been described is as collections of nodes that maximise a local quality function. For a particular set of structures, we search the space of quality functions using Genetic Programming, to find a function that locally describes that set of structures. This technique allows us to investigate the common network properties of defined sets of structures. We also use this technique to classify and differentiate between different types of structure. We apply this method on several synthetic benchmarks, and on a protein-protein interaction network. Our results indicate this is a useful technique of investigating properties that sets of network structures have in common.