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Ian C. Carroll

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Dynamic Topological Data Analysis for Brain Networks via Wasserstein Graph Clustering

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Jan 11, 2022
Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith

Figure 1 for Dynamic Topological Data Analysis for Brain Networks via Wasserstein Graph Clustering
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We present the novel Wasserstein graph clustering for dynamically changing graphs. The Wasserstein clustering penalizes the topological discrepancy between graphs. The Wasserstein clustering is shown to outperform the widely used k-means clustering. The method applied in more accurate determination of the state spaces of dynamically changing functional brain networks.

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Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering

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Jan 01, 2022
Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith

Figure 1 for Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering
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Figure 3 for Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering
Figure 4 for Dynamic Persistent Homology for Brain Networks via Wasserstein Graph Clustering

We present the novel Wasserstein graph clustering for dynamically changing graphs. The Wasserstein clustering penalizes the topological discrepancy between graphs. The Wasserstein clustering is shown to outperform the widely used k-means clustering. The method applied in more accurate determination of the state spaces of dynamically changing functional brain networks.

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Github IconAccess Paper or Ask Questions
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