Counting Subgraphs in Somewhere Dense Graphs

We study the problems of counting copies and induced copies of a small pattern graph H in a large host graph G. Recent work fully classified the complexity of those problems according to structural restrictions on the patterns H. In this work, we address the more challenging task of analyzing the complexity for restricted patterns and restricted hosts. Specifically, we ask which families of allowed patterns and hosts imply fixed-parameter tractability, i.e., the existence of an algorithm running in time f(H)⋅|G|O(1) for some computable function f. Our main results present exhaustive and explicit complexity classifications for families that satisfy natural closure properties. Among others, we identify the problems of counting small matchings and independent sets in subgraph-closed graph classes G as our central objects of study and establish the following crisp dichotomies as consequences of the exponential time hypothesis: (1) Counting k-matchings in a graph G∈G is fixed-parameter tractable if and only if G is nowhere dense. (2) Counting k-independent sets in a graph G∈G is fixed-parameter tractable if and only if G is nowhere dense. Moreover, we obtain almost tight conditional lower bounds if G is somewhere dense, i.e., not nowhere dense. These base cases of our classifications subsume a wide variety of previous results on the matching and independent set problem, such as counting k-matchings in bipartite graphs (Curticapean, Marx; FOCS 14), in F-colorable graphs (Roth, Wellnitz; SODA 20), and in degenerate graphs (Bressan, Roth; FOCS 21), as well as counting k-independent sets in bipartite graphs (Curticapean et al.; Algorithmica 19). At the same time, our proofs are much simpler: using structural characterizations of somewhere dense graphs, we show that a colorful version of a recent breakthrough technique for analyzing pattern counting problems (Curticapean, Dell, Marx; STOC 17) applies to any subgraph-closed somewhere dense class of graphs, yielding a unified view of our current understanding of the complexity of subgraph counting.