🤖 AI Summary
This work addresses the limited accuracy of upper bound estimation for the Maximum Clique Problem (MCP) by proposing a novel framework that integrates upper bounding functions with graph reduction techniques. The approach introduces adjustable reduction rules—namely $(k,\omega^u)$-core, $(k,\omega^u)$-truss, and the more general $(k,d,\omega^u)$-truss—by embedding upper bound tests into core and truss decompositions for the first time, and further enhances pruning power through struction operations while maintaining computational efficiency. Experimental results demonstrate that the method significantly improves upon multiple classical upper bounds across 73 benchmark graphs. Notably, it reduces certified integer upper bounds to 73, 115, and 168 on the challenging DIMACS instances C500.9, C1000.9, and C2000.9, respectively, and achieves comparable accuracy faster than direct semidefinite programming (SDP) on sparse graphs.
📝 Abstract
We study the interaction between reduction rules and upper-bound functions for the Maximum Clique Problem (MCP). We show how MCP upper-bound functions can strengthen classical core and truss reductions by replacing local size conditions with upper-bound tests. This leads to the \((k,ω^u)\)-core, the \((k,ω^u)\)-truss, and the more general \((k,d,ω^u)\)-truss, where the parameter \(d\) controls the trade-off between stronger reductions and additional computational cost. For each of these notions, we prove clique-preservation properties, correctness of the corresponding peeling algorithm, and running-time bounds. Based on these reductions, we introduce a general framework for improving upper-bound values for MCP. We give two concrete instantiations of the framework: one that uses only the combined truss and core reductions, and one that combines the truss and core reductions with repeated applications of structions. Computational experiments on 73 benchmark graphs show that the proposed reductions can substantially improve several standard upper-bound functions and that combining multiple reduction methods can be beneficial in practice. In particular, the combination of structions, truss and core reductions with a DSatur-based bound often reached SDP-level upper-bound values faster than direct SDP computation; on the tested graphs with edge density below \(0.7\), it did so in every case. Using the truss and core reduction with the Lovász theta upper-bound function, we also improve the previously best certified integer upper-bound values for three difficult DIMACS instances whose exact clique numbers are not known. In particular, we improve upper-bound values for graph
\texttt{C500.9} from 83 to 73, for graph \texttt{C1000.9} from 122 to 115, and for graph \texttt{C2000.9} from 177 to 168.