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Updated: Feb 5, 2026
Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
Published on: April 15, 2015
SuperNoder: a tool to discover over-represented modular structures in networks
Danilo Dessì1, Jacopo Cirrone2, Diego Reforgiato Recupero3
1Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09124, Italy. danilo_dessi@unica.it.
Background:
Networks whose nodes have labels can seem complex. Fortunately, many have substructures that occur often ("motifs"). A societal example of a motif might be a household. Replacing such motifs by named supernodes reduces the complexity of the network and can bring out insightful features. Doing so repeatedly may give hints about higher level structures of the network. We call this recursive process Recursive Supernode Extraction.
Results:
This paper describes algorithms and a tool to discover disjoint (i.e. non-overlapping) motifs in a network, replacing those motifs by new nodes, and then recursing. We show applications in food-web and protein-protein interaction (PPI) networks where our methods reduce the complexity of the network and yield insights.
Conclusions:
SuperNoder is a web-based and standalone tool which enables the simplification of big graphs based on the reduction of high frequency motifs. It applies various strategies for identifying disjoint motifs with the goal of enhancing the understandability of networks.
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