MAVisto: a tool for the exploration of network motifs
Bioinformatics
FANMOD: a tool for fast network motif detection
Bioinformatics
Cyberlearners and learning resources
Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
Self-stabilizing algorithm for maximal graph partitioning into triangles
SSS'12 Proceedings of the 14th international conference on Stabilization, Safety, and Security of Distributed Systems
Self-stabilizing algorithm for maximal graph decomposition into disjoint paths of fixed length
Proceedings of the 4th International Workshop on Theoretical Aspects of Dynamic Distributed Systems
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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The study of the sub-structure of complex networks is of major importance to relate topology and functionality. Many efforts have been devoted to the analysis of the modular structure of networks using the quality function known as modularity. However, generally speaking, the relation between topological modules and functional groups is still unknown, and depends on the semantic of the links. Sometimes, we know in advance that many connections are transitive, and as a consequence, triangles have a specific meaning. Here we propose the study of the modular structure of networks considering triangles as the building blocks of modules. The method generalizes the standard modularity and uses spectral optimization to find its maximum. We compare the partitions obtained with those resulting from the optimization of the standard modularity in several real networks. The results show that the information reported by the analysis of modules of triangles complements the information of the classical modularity analysis.