Abstract
Community structure is one of the important features of complex brain network. Recently, major efforts have been made to investigate the non-overlapping community structure of brain network. However, an important fact is often ignored that the community structures of most real networks are overlapping. In this paper, we propose a novel method called sparse symmetric non-negative matrix factorization (ssNMF) to detect the overlapping community structure of the brain functional network, by adding a sparse constraint on the standard symmetric NMF (symNMF). Besides, we apply a sparse-network based framework by using non-negative adaptive sparse representation (NASR) to construct a sparse brain network. Simulated fMRI experimental results show that NMF-based methods achieve higher accuracy than methods of modularity optimization, normalized cuts and affinity propagation. Results of real fMRI experiments also lead to meaningful findings, which can help to promote the understanding of brain functional systems.
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Acknowledgements
This work was supported in part by the National Basic Research Program of China under Grant 2015CB351704, the National Natural Science Foundation of China under Grant 61375118, and the Research Foundation for Young Teachers in Anhui University of Technology under Grant QZ201516.
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Li, X., Hu, Z., Wang, H. (2016). Sparse-Network Based Framework for Detecting the Overlapping Community Structure of Brain Functional Network. In: Liu, CL., Hussain, A., Luo, B., Tan, K., Zeng, Y., Zhang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2016. Lecture Notes in Computer Science(), vol 10023. Springer, Cham. https://doi.org/10.1007/978-3-319-49685-6_32
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DOI: https://doi.org/10.1007/978-3-319-49685-6_32
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