Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Effective Algorithm for Detecting Community Structure in Complex Networks Based on GA and Clustering
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Exploring local community structures in large networks
Web Intelligence and Agent Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Community detection in complex networks using collaborative evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
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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.