Faster sublinear approximations of k-cliques for low arboricity graphs

Given query access to an undirected graph G, we consider the problem of computing a (1±ϵ)-approximation of the number of k-cliques in G. The standard query model for general graphs allows for degree queries, neighbor queries, and pair queries. Let n be the number of vertices, m be the number of edges, and n_k be the number of k-cliques. Previous work by Eden, Ron and Seshadhri (STOC 2018) gives an O^*(n/n^1/k_k + m^k/2/n_k)-time algorithm for this problem (we use O^*(·) to suppress (log n, 1/ϵ, k^k) dependencies). Moreover, this bound is nearly optimal when the expression is sublinear in the size of the graph. Our motivation is to circumvent this lower bound, by parameterizing the complexity in terms of graph arboricity. The arboricity of G is a measure for the graph density "everywhere". We design an algorithm for the class of graphs with arboricity at most α, whose running time is O^*(min{nα^k-1/n_k, n/n_k^1/k+m α^k-2/n_k}). We also prove a nearly matching lower bound. For all graphs, the arboricity is O(√(m)), so this bound subsumes all previous results on sublinear clique approximation. As a special case of interest, consider minor-closed families of graphs, which have constant arboricity. Our result implies that for any minor-closed family of graphs, there is a (1±ϵ)-approximation algorithm for n_k that has running time O^*(n/n_k). Such a bound was not known even for the special (classic) case of triangle counting in planar graphs.