Deterministic Streaming Lower Bounds for Approximate Maximum Clique and Maximum Independent Set

Abstract

We study the canonical \textsf{Maximum Clique} and \textsf{Maximum Independent Set} problems in the one-pass edge-arrival graph streaming setting. Here, the edges of some input graph $G = (V,E)$ are presented one at a time (possibly including deletions), before an algorithm needs to produce either a large clique or independent set at the end of the stream, with the focus being on space complexity. We are interested in finding $\beta$-approximate solutions, for any $\beta \geq 1$. Previous work gave an algorithm using $\tilde{O}\left(n^2/\beta^2\right)$ bits of space, together with a corresponding $\tilde{\Omega}\left(n^2/\beta^2\right)$ two-party communication lower bound [Halld\'orsson et al., ICALP'12], seeming to resolve the problem. However, their algorithm crucially relies on randomness, and the best known deterministic algorithm remains a folklore derandomisation using $O\left(n^2/\beta\right)$ bits of space, leaving a (deterministic) gap of size $\tilde{O}(\beta)$. We resolve this deterministic gap with an (almost) tight lower bound: any deterministic algorithm for either problem must use $\Omega\left(\frac{n^2}{\beta\cdot\log n}\right)$ bits of space. Our proof is via a two-party one-way communication lower bound, and highlights the power of randomness when approaching either of these problems.