Name
Title | ||
|---|---|---|
Community-Preserving Graph Convolutions For Structural And Functional Joint Embedding Of Brain Networks |
Abstract | ||
|---|---|---|
We propose a framework of Siamese community-preserving graph convolutional network (SCP-GCN) to learn the structural and functional joint embedding of brain networks. Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity. Moreover, we propose to preserve the community structure of brain networks in the graph convolutions by considering the intra-community and inter-community properties in the learning process. Furthermore, we use Siamese architecture which models the pair-wise similarity learning to guide the learning process. To evaluate the proposed approach, we conduct extensive experiments on two real brain network datasets. The experimental results demonstrate the superior performance of the proposed approach in structural and functional joint embedding for neurological disorder analysis, indicating its promising value for clinical applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9005586 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Keywords | Field | DocType |
Graph Neural Networks, Community-preserving Graph Convolutions, Siamese Network, Brain Network Analysis | Similarity learning,Brain network,Graph,Embedding,Computer science,Convolution,Graph neural networks,Theoretical computer science,Artificial intelligence,Machine learning | Conference |
ISSN | Citations | PageRank |
2639-1589 | 1 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiahao Liu | 1 | 1 | 0.34 |
| Guixiang Ma | 2 | 39 | 4.74 |
| Fei Jiang | 3 | 1 | 0.34 |
| Chun-Ta Lu | 4 | 183 | 15.10 |
| Philip S. Yu | 5 | 30670 | 3474.16 |
| Ann Ragin | 6 | 1 | 0.34 |