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Unfolding Kernel Embeddings of Graphs : Enhancing Class Separation through Manifold Learning

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Figure

Figure 1: The MDS embeddings of the shortest-path [1], graphlet [2], random walk [3] and Weisfeiler-Lehman [4] kernels (with h = 1, 2, 3) on the COIL dataset.
Figure 2: Example of reduced linear separability due to high curvature of the embedding
Figure 3: 3D plot of the 10-fold cross validation accuracy (with C = Copt) of the graph kernels on the Shock
Table 1: Information on the graph datasets
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