Subgraph Networks With Application to Structural Feature Space Expansion

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct motifs use only their numbers to characterize the underlying network. Although such statistics can be used describe a network model, or even design some algorithms, role of in applications further explored so improve results. In this article, concept subgraph (SGN) is introduced then applied models, algorithms designed for constructing 1st-order 2nd-order SGNs, which easily extended build higher-order ones. Furthermore, these SGNs are expand structural feature space network, beneficial classification. Numerical experiments demonstrate that classification model based on features original together always performs best compared models one two networks. other words, complement better classification, regardless extraction method used, handcrafted, embedding kernel-based methods.