TL;DR
This work presents a novel algorithm named HP-MC that, like prior approaches, is based on the branch and bound search, but which outperforms the leading maximum clique codes on large sparse graphs of various topologies by up to 10x on average.
This work proposes a simple and effective column-selective graph sampling algorithm based on a minimum inner product greedy selection rule, well-suited for large-scale graphs where the full Laplacian cannot be stored in memory.
The findings indicate that the Eppstein-Wang algorithm provides a practical and scalable solution for average distance estimation, with higher reliability on unipartite graphs compared to bipartite graphs.
The Ramsey number $R(m,n)$ is the smallest order at which every red-blue edge coloring of a complete graph must contain a blue clique (a complete subgraph) of size $m$ or a red clique of size $n$. Determining these numbers exactly is extremely hard, and even certifying a lower bound requires exhibiting an explicit colo...
CP-McSplitDAL is introduced, a cooperative parallel framework that extends McSplit-DAL with portfolio-style multi-heuristic search on shared-memory machines and achieves lower regret in time to optimality, improves solution quality under time limits, and better exploits multi-core hardware than non-cooperative or purel...
Investigating the energy complexity of several well-studied (local) problems in distributed graph algorithms—namely, matching and vertex cover approximations, spanners, low-outdegree orientations, and set cover, presents randomized distributed algorithms that achieve nearly exponentially smaller energy complexity.
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