Counting HyperGraphlets via Color Coding: a Quadratic Barrier and How to Break It
This addresses a fundamental algorithmic challenge in hypergraph analysis for researchers in computational theory and data science, though it is incremental as it builds on color coding with a new property.
The paper tackled the problem of counting k-hypergraphlets in hypergraphs, showing that color coding encounters a quadratic barrier under the Orthogonal Vector Conjecture, and introduced an algorithm based on (α,β)-niceness that runs in time 2^O(k)·(2^β|V| + α^k|E| + α^2β|H|), outperforming naive methods by more than an order of magnitude in experiments.
We study the problem of counting $k$-\emph{hyper}graphlets, an interesting but surprisingly ignored primitive, with the aim of understanding if efficient algorithms exist. To this end we consider \emph{color coding}, a well-known technique for approximately counting $k$-graphlets in graphs. Our first result is that, on hypergraphs, color coding encounters a \emph{quadratic barrier}: under the Orthogonal Vector Conjecture, no implementation of it can run in time sub-quadratic in the size of the input. We then introduce a simple property, $(α,β)$-niceness, that hypergraphs from real-world datasets appear to satisfy for small values of $α$ and $β$. Intuitively, an $(α,β)$-nice hypergraph can be split into two sub-hypergraphs having respectively rank at most $α$ and degree at most $β$. By applying different techniques to each sub-hypergraph and carefully combining the outputs, we show how to run color coding in time $2^{O(k)} \cdot \big(2^β|V| + α^k |E| + α^2 β\size{H}\big)$, where $H=(V,E)$ is the input hypergraph. Afterwards, we can sample colorful $k$-hypergraphlets uniformly in expected $k^{O(k)} \cdot (β^2+\ln |V|)$ time per sample. Experiments on real-world hypergraphs show that our algorithm neatly outperforms the naive quadratic algorithm, sometimes by more than an order of magnitude.