Generating functionology
Implementing progress indicators for recursive algorithms
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
A Fast Approximation Algorithm for Computing theFrequencies of Subgraphs in a Given Graph
SIAM Journal on Computing
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Concrete Mathematics: A Foundation for Computer Science
Concrete Mathematics: A Foundation for Computer Science
FANMOD: a tool for fast network motif detection
Bioinformatics
A multi-layered approach to protein data integration for diabetes research
Artificial Intelligence in Medicine
Parameterized Algorithms and Hardness Results for Some Graph Motif Problems
CPM '08 Proceedings of the 19th annual symposium on Combinatorial Pattern Matching
Local Topology of Social Network Based on Motif Analysis
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Temporal Changes in Connection Patterns of an Email-Based Social Network
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Approximating the Number of Network Motifs
WAW '09 Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph
Structural Changes in an Email-Based Social Network
KES-AMSTA '09 Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
Exploiting knowledge ontology and software agents for PPI network analysis
Expert Systems with Applications: An International Journal
Evolutionary search for improved path diagrams
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
g-tries: an efficient data structure for discovering network motifs
Proceedings of the 2010 ACM Symposium on Applied Computing
IEEE Transactions on Information Technology in Biomedicine
Frequent subgraph discovery in dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
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
Applying wearable solutions in dependent environments
IEEE Transactions on Information Technology in Biomedicine
eBay: an E-commerce marketplace as a complex network
Proceedings of the fourth ACM international conference on Web search and data mining
Motif-based attack detection in network communication graphs
CMS'11 Proceedings of the 12th IFIP TC 6/TC 11 international conference on Communications and multimedia security
Parallel discovery of network motifs
Journal of Parallel and Distributed Computing
RAGE - A rapid graphlet enumerator for large networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
An algorithm for network motif discovery in biological networks
International Journal of Data Mining and Bioinformatics
Network-theoretic classification of parallel computation patterns
International Journal of High Performance Computing Applications
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)
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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Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from a sampling bias and scales poorly with increasing subgraph size. Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Furthermore, we present an efficient new approach for estimating the frequency of subgraphs in random networks that, in contrast to previous approaches, does not require the explicit generation of random networks. Experiments on a testbed of biological networks show our new algorithms to be orders of magnitude faster than previous approaches, allowing for the detection of larger motifs in bigger networks than previously possible and thus facilitating deeper insight into the field.