  Graphs are naturally used to describe the structures of various real-world
systems in biology, society, computer science etc., where subgraphs or motifs
as basic blocks play an important role in function expression and information
processing. However, existing research focuses on the basic statistics of
certain motifs, largely ignoring the connection patterns among them. Recently,
a subgraph network (SGN) model is proposed to study the potential structure
among motifs, and it was found that the integration of SGN can enhance a series
of graph classification methods. However, SGN model lacks diversity and is of
quite high time complexity, making it difficult to widely apply in practice. In
this paper, we introduce sampling strategies into SGN, and design a novel
sampling subgraph network model, which is scale-controllable and of higher
diversity. We also present a hierarchical feature fusion framework to integrate
the structural features of diverse sampling SGNs, so as to improve the
performance of graph classification. Extensive experiments demonstrate that, by
comparing with the SGN model, our new model indeed has much lower time
complexity (reduced by two orders of magnitude) and can better enhance a series
of graph classification methods (doubling the performance enhancement).
