Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
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
ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
Fast Parallel Expectation Maximization for Gaussian Mixture Models on GPUs Using CUDA
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Signed networks in social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Stochastic Network Motif Detection in Social Media
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Detecting stochastic temporal network motifs for human communication patterns analysis
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Network motif detection methods are known to be important for studying the structural properties embedded in network data. Extending them to stochastic ones help capture the interaction uncertainties in stochastic networks. In this paper, we propose a finite mixture model to detect multiple stochastic motifs in network data with the conjecture that interactions to be modeled in the motifs are of stochastic nature. Component-wise Expectation Maximization algorithm is employed so that both the optimal number of motifs and the parameters of their corresponding probabilistic models can be estimated. For evaluating the effectiveness of the algorithm, we applied the stochastic motif detection algorithm to both synthetic and benchmark datasets. Also, we discuss how the obtained stochastic motifs could help the domain experts to gain better insights on the over-represented patterns in the network data.