  Previous work has suggested that the structural restrictions of graphs from
classes of bounded expansion--locally dense pockets in a globally sparse
graph--naturally coincide with common properties of real-world networks such as
clustering and heavy-tailed degree distributions. As such, fixed-parameter
tractable algorithms for bounded expansion classes may offer a promising
framework for network analysis where other approaches have struggled to scale.
However, there has been little work done in implementing and evaluating the
performance of these structure-based algorithms. To this end we introduce
CONCUSS, a proof-of-concept implementation of a generic algorithmic pipeline
for classes of bounded expansion. In particular, we focus on using CONCUSS for
subgraph isomorphism counting (also called motif or graphlet counting), which
has been used extensively as a tool for analyzing biological and social
networks. Through a broad set of experiments we first evaluate the interactions
between implementation/engineering choices at multiple stages of the pipeline
and their effects on overall run time. From there, we establish viability of
the bounded expansion framework by demonstrating that in some scenarios CONCUSS
achieves run times competitive with a popular algorithm for subgraph
isomorphism counting that does not exploit graph structure. Finally, we
empirically identify two particular ways in which future theoretical advances
could alleviate bottlenecks in the algorithmic pipeline.
