Isomorph-free exhaustive generation
Journal of Algorithms
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
An important connection between network motifs and parsimony models
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Approximating the Number of Network Motifs
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Quantifying systemic evolutionary changes by color coding confidence-scored PPI networks
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Counting stars and other small subgraphs in sublinear time
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Algorithms and theory of computation handbook
Efficient subgraph frequency estimation with g-tries
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Parallel discovery of network motifs
Journal of Parallel and Distributed Computing
Survey: Computational challenges in systems biology
Computer Science Review
Querying subgraph sets with g-tries
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
Symmetry Compression Method for Discovering Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards a faster network-centric subgraph census
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
G-Tries: a data structure for storing and finding subgraphs
Data Mining and Knowledge Discovery
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The study of biological networks and network motifs can yield significant new insights into systems biology. Previous methods of discovering network motifs - network-centric subgraph enumeration and sampling - have been limited to motifs of 6 to 8 nodes, revealing only the smallest network components. New methods are necessary to identify larger network sub-structures and functional motifs. Here we present a novel algorithm for discovering large network motifs that achieves these goals, based on a novel symmetry-breaking technique, which eliminates repeated isomorphism testing, leading to an exponential speed-up over previous methods. This technique is made possible by reversing the traditional network-based search at the heart of the algorithm to a motif-based search, which also eliminates the need to store all motifs of a given size and enables parallelization and scaling. Additionally, our method enables us to study the clustering properties of discovered motifs, revealing even larger network elements. We apply this algorithm to the protein-protein interaction network and transcription regulatory network of S. cerevisiae, and discover several large network motifs, which were previously inaccessible to existing methods, including a 29-node cluster of 15-node motifs corresponding to the key transcription machinery of S. cerevisiae.