Name
Title | ||
|---|---|---|
Sparse-Network Based Framework for Detecting the Overlapping Community Structure of Brain Functional Network. |
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. |
Year | Venue | Field |
|---|---|---|
2016 | BICS | Data mining,Brain network,Community structure,Normalization (statistics),Affinity propagation,Computer science,Matrix decomposition,Sparse approximation,Non-negative matrix factorization,Modularity |
DocType | Citations | PageRank |
Conference | 2 | 0.36 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Li | 1 | 12 | 5.65 |
| Zilan Hu | 2 | 2 | 0.36 |
| Haixian Wang | 3 | 39 | 7.60 |