Practical maximum clique search on 300–900 vertex graphs in a few seconds

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Practical maximum clique search on 300–900 vertex graphs with only a few seconds available

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I am looking for guidance on a practical maximum clique problem.

Suppose I am given an undirected graph $G=(V,E)$, usually with about $300$ to $900$ vertices. I need to find a clique $C \subseteq V$, meaning that for every two distinct vertices $u,v \in C$, the edge $(u,v)\in E$.

The ideal goal is to find a maximum clique. In my setting, however, there is a short wall-clock deadline, usually around $6$ to $30$ seconds. So in practice, I need to return the largest clique I can find before the deadline.

The returned clique also needs to be maximal. By this I mean that there should be no vertex $w \in V \setminus C$ that can be added to $C$ while keeping it a clique. So returning just any maximal clique is not useful; the clique needs to be large, ideally close to maximum.

I understand the basic difficulty: the maximum clique problem is NP-hard, and brute force over all vertex subsets is impossible here, since there are $2^{|V|}$ possible subsets.

I have seen several kinds of methods mentioned:

branch and bound with coloring bounds;

Bron--Kerbosch-style algorithms;

bitset-based exact search;

greedy construction;

local search or tabu search;

randomized restarts;

hybrid methods.

The part I am most unsure about is what one should do when the time limit is very short. Since the total deadline is only a few seconds, I cannot spend much time testing many different algorithms on the current graph. Ideally, I would like to decide very quickly, perhaps within the first $0.1$ to $0.5$ seconds, which approach to run first.

My questions are:

For graphs with about $300$--$900$ vertices and time limits of $6$--$30$ seconds, what maximum clique methods are considered strongest in practice?

If I only need a very large clique and do not need to prove optimality, are exact branch-and-bound methods still a good starting point, or are local-search / tabu-search methods usually better?

Are there cheap graph statistics, such as edge density, degree distribution, degeneracy, clustering, or approximate coloring number, that help decide which solver family to try first?

Is there literature on algorithm selection or solver portfolios for maximum clique, similar to what exists for SAT and other combinatorial optimization problems?

Are there well-known benchmark papers or implementations that would be good starting points before writing my own solver?

I am not asking anyone to solve a particular graph instance. I am trying to understand what algorithms and references are considered standard for this practical regime, especially when the time limit is too short to run many different approaches.

reference-request<br>co.combinatorics<br>graph-theory<br>algorithms<br>combinatorial-optimization

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