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These measures comprise multiple side views.
We presented an approach for selecting discriminative subgraph features using multiple side views.
gMSV: The proposed discriminative subgraph selection method using multiple side views.
(gSide)Let ('{mathcal{D}}={G_1,ldots,G_n}) denote a graph dataset with multiple side views.
The experiments demonstrate that our subgraph selection approach using multiple side views can effectively boost graph classification performances.
The focus of this paper is to investigate side information consistency and explore multiple side views in discriminative subgraph selection.
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In contrast to existing subgraph mining approaches that focus on the graph view alone, the proposed method can explore multiple vector-based side views to find an optimal set of subgraph features for graph classification.
In contrast to existing subgraph mining approaches that focus on a single view of the graph representation, our method can explore multiple vector-based side views to find an optimal set of subgraph features for graph classification.
Stipules, side views; D, D'.
a d Top views; e h side views.
Bottom insets show the corresponding side views.
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