Detecting Large Quasi-cliques on Dynamic Networks

Motivated by the problem of detecting large and cohesive groups of vertices in real networks, the task of finding large \emph{quasi-cliques} has attracted considerable attention across different research areas. From a computational complexity perspective, strong inapproximability results are known for this problem, yet several heuristics have been proposed to identify large quasi-cliques in real-world networks. Recently, [Pang \emph{et al.}, (WWW 2024)] introduced a similarity-based approach that represents the current state of the art. In this work, we extend that approach to \emph{dynamic} networks, thereby addressing an open problem posed by [Pang \emph{et al.}, (WWW 2024)]. We first present a Baseline fully dynamic algorithm where edges of the network can be both inserted and deleted. The algorithm exactly maintains the same quasi-clique returned by the algorithm by Pang et al. on the current graph, with update time $\widetilde{O}(Δ)$, where $Δ$ is the maximum degree. We then focus on the practically relevant incremental case, where only edge insertions are allowed, and design an algorithm with $O(\log Δ)$ update time. This method leverages a novel technique for dynamically maintaining accurate estimates of vertex $γ$-degrees, a core component of framework by Pang et al., and achieves up to $207\times$ speed-up over the Baseline while preserving comparable solution quality. Finally, we extend the approach to the fully dynamic setting, supporting both insertions and deletions, obtaining up to $21\times$ speed-up with limited and acceptable loss in quasi-clique size and density. We provide a formal analysis of our algorithms and validate them through an extensive set of experiments on real-world datasets.