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Network features were computed for each subgraph, generating a machine-learning model.
2
This approach is applied here to generalize the subgraph centrality of nodes in complex networks.
3
We present an efficient constrained subgraph mining algorithm to discover structure motifs in this setting.
4
We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures.
5
We here present a method of clearly identifying multipartite subgraphs in a network.
6
The model on the right is hard to shard, because there are no such subgraphs.
7
Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp.