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Published in:
2013 | OriginalPaper | Chapter
Motif Identification Based on Local Structure Clustering
Authors : Junying Zhang, Yuling Xue
Published in: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013
Publisher: Springer Berlin Heidelberg
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Abstract
Network motif identification is significant in that motifs generally reflect functionalities. However, the task is greatly challenging due to diverse patterns of motifs possibly existent in a network and high computation complexity for large scale networks. In this study, we propose a network motif identification method, FCMD, based on clustering subgraphs according to their local structures. By modeling local-topological feature of a network with a feature vector, the approach maps motif identification problem into a clustering problem of feature vectors in a feature space, which greatly reduces computation complexity hence facilitates very large scale networks. Experiments on 8 real networks, including biochemical network, neural network, electronics circuit network, et al., indicates that the proposed method is very effective in motif detection with computation complexity approximately independent to the scale of the network.