NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Assessing data mining results via swap randomization
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A multi-layered approach to protein data integration for diabetes research
Artificial Intelligence in Medicine
Assessing data mining results via swap randomization
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
Stability cannot be derived from local structure in biochemical networks
Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems
Stability from structure: metabolic networks are unlike other biological networks
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
The h-Index of a Graph and Its Application to Dynamic Subgraph Statistics
WADS '09 Proceedings of the 11th International Symposium on Algorithms and Data Structures
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
IEEE Transactions on Information Technology in Biomedicine
Frequent subgraph discovery in dynamic networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Frequent subgraph mining on a single large graph using sampling techniques
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
Efficient subgraph frequency estimation with g-tries
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
eBay: an E-commerce marketplace as a complex network
Proceedings of the fourth ACM international conference on Web search and data mining
All normalized anti-monotonic overlap graph measures are bounded
Data Mining and Knowledge Discovery
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
Discovering the Evolutionary Patterns in Local Topology of an E-Mail Social Network
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Parallel discovery of network motifs
Journal of Parallel and Distributed Computing
Property-Driven statistics of biological networks
Transactions on Computational Systems Biology VI
A faster algorithm for detecting network motifs
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
An algorithm for network motif discovery in biological networks
International Journal of Data Mining and Bioinformatics
Survey: Computational challenges in systems biology
Computer Science Review
Tutorial on biological networks
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Detecting multiple stochastic network motifs in network data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Sampling connected induced subgraphs uniformly at random
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
A framework for evaluating the smoothness of data-mining results
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Symmetry Compression Method for Discovering Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
"Tri, tri again": finding triangles and small subgraphs in a distributed setting
DISC'12 Proceedings of the 26th international conference on Distributed Computing
A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
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
| Hi-index | 3.84 |
Summary: Biological and engineered networks have recently been shown to display network motifs: a small set of characteristic patterns that occur much more frequently than in randomized networks with the same degree sequence. Network motifs were demonstrated to play key information processing roles in biological regulation networks. Existing algorithms for detecting network motifs act by exhaustively enumerating all subgraphs with a given number of nodes in the network. The runtime of such algorithms increases strongly with network size. Here, we present a novel algorithm that allows estimation of subgraph concentrations and detection of network motifs at a runtime that is asymptotically independent of the network size. This algorithm is based on random sampling of subgraphs. Network motifs are detected with a surprisingly small number of samples in a wide variety of networks. Our method can be applied to estimate the concentrations of larger subgraphs in larger networks than was previously possible with exhaustive enumeration algorithms. We present results for high-order motifs in several biological networks and discuss their possible functions. Availability: A software tool for estimating subgraph concentrations and detecting network motifs (mfinder 1.1) and further information is available at http://www.weizmann.ac.il/mcb/UriAlon/