Self-Organization Patterns in Wasp and Open Source Communities
IEEE Intelligent Systems
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
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
Efficient subgraph frequency estimation with g-tries
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
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
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
Studying Diffusion of Viral Content at Dyadic Level
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Dissemination Patterns and Associated Network Effects of Sentiments in Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Classifying Wikipedia articles using network motif counts and ratios
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Network motifs --- small subgraphs that reflect local topology can be used to discover general profile and properties of the network. Analysis of motifs for the large social networks derived from email communication is presented in the paper. The distribution of motifs in all analyzed real social networks is very similar one another and can be treated as the network fingerprint. This property is most distinctive for stronger human relationships.