HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Asynchronous Random Polling Dynamic Load Balancing
ISAAC '99 Proceedings of the 10th International Symposium on Algorithms and Computation
Parallel algorithms for mining frequent structural motifs in scientific data
Proceedings of the 18th annual international conference on Supercomputing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Parallel Algorithm for Extracting Transcription Regulatory Network Motifs
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Strategies for Network Motifs Discovery
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
Network motif discovery using subgraph enumeration and symmetry-breaking
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
g-tries: an efficient data structure for discovering network motifs
Proceedings of the 2010 ACM Symposium on Applied Computing
Efficient Parallel Subgraph Counting Using G-Tries
CLUSTER '10 Proceedings of the 2010 IEEE International Conference on Cluster Computing
G-Tries: a data structure for storing and finding subgraphs
Data Mining and Knowledge Discovery
Effective connectivity analysis of fMRI data based on network motifs
The Journal of Supercomputing
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Many natural structures can be naturally represented by complex networks. Discovering network motifs, which are overrepresented patterns of inter-connections, is a computationally hard task related to graph isomorphism. Sequential methods are hindered by an exponential execution time growth when we increase the size of motifs and networks. In this article we study the opportunities for parallelism in existing methods and propose new parallel strategies that adapt and extend one of the most efficient serial methods known from the Fanmod tool. We propose both a master-worker strategy and one with distributed control, in which we employ a randomized receiver initiated methodology capable of providing dynamic load balancing during the whole computation process. Our strategies are capable of dealing both with exact and approximate network motif discovery. We implement and apply our algorithms to a set of representative networks and examine their scalability up to 128 processing cores. We obtain almost linear speedups, showcasing the efficiency of our proposed approach and are able to reach motif sizes that were not previously achievable using conventional serial algorithms.