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Network inference is a rapidly advancing field, with new methods beingproposed on a regular basis. Understanding the advantages and limitations ofdifferent network inference methods is key to their effective application indifferent circumstances. The common structural properties shared by diversenetworks naturally pose a challenge when it comes to devising accurateinference methods, but surprisingly, there is a paucity of comparison andevaluation methods. Historically, every new methodology has only been testedagainst \textit{gold standard} (true values) purpose-designed synthetic andreal-world (validated) biological networks. In this paper we aim to assess theimpact of taking into consideration aspects of topological and informationcontent in the evaluation of the final accuracy of an inference procedure.Specifically, we will compare the best inference methods, in bothgraph-theoretic and information-theoretic terms, for preserving topologicalproperties and the original information content of synthetic and biologicalnetworks. New methods for performance comparison are introduced by borrowingideas from gene set enrichment analysis and by applying concepts fromalgorithmic complexity. Experimental results show that no individual algorithmoutperforms all others in all cases, and that the challenging and non-trivialnature of network inference is evident in the struggle of some of thealgorithms to turn in a performance that is superior to random guesswork.Therefore special care should be taken to suit the method to the purpose athand. Finally, we show that evaluations from data generated using differentunderlying topologies have different signatures that can be used to betterchoose a network reconstruction method.

Evaluating Network Inference Methods in Terms of Their Ability to Preserve the Topology and Complexity of Genetic Networks