Listing Minimal Cores in Large Real-World Graphs
Cohesive subgraph mining is a fundamental task in graph data analytics. We re-visit the problem of listing all minimal $k$-cores, where a $k$-core is a subgraph in which every vertex has degree at least $k$, and minimality requires that no proper subset remains a $k$-core. Existing methods are computationally prohibitive due to explosive branching and costly branch state update, leading to the trivial worst-case bound $O^{*}\left(2^{n}\right)$ for the basic branch-and-bound baseline wh, where $O^{*}$ suppresses polynomial factors and $n$ is the number of vertices. In this paper, we present an improved method IMinC based on three key ideas: (i) a principled branching state with lineartime update; (ii) a pivot strategy that guides branching toward promising vertices; and (iii) a divide-and-conquer framework that initializes each subproblem to enable our pivot strategy throughout and reduce recursion depth. We further introduce three reduction rules that aggressively prune infeasible branches. Together, these components yield the worst-case time complexity of $O^{*}\left(\alpha_{\ell}^{n}\right)$, where $\alpha_{\ell}$ is a positive number strictly smaller than 2. We also extend IMinC to list minimal $k$-cores under a size bound, addressing practical needs such as size-bounded community search. Extensive experiments on 12 real-world graphs demonstrate that IMinC outperforms the baselines by up to 2 order of magnitude, delivering substantial gains in efficiency.