The problem of finding the degeneracy of a graph is a subproblem of the k-core decomposition problem. In this paper, we present a (1 + epsilon)-approximate solution to the degeneracy problem which runs in O(n log n) time, sublinear in the input size for dense graphs, by sampling a small number of neighbors adjacent to high degree nodes. Our algorithm can also be extended to an O(n log n) time solution to the k-core decomposition problem. This improves upon the method by Bhattacharya et al., which implies a (4 + epsilon)-approximate ~O(n) solution to the degeneracy problem, and our techniques are similar to other sketching methods which use sublinear space for k-core and degeneracy. We prove theoretical guarantees of our algorithm and provide optimizations, which improve the running time of our algorithm in practice. Experiments on massive real-world web graphs show that our algorithm performs significantly faster than previous methods for computing degeneracy, including the 2022 exact degeneracy algorithm by Li et al.
翻译:找到一个图形的变异性的问题是 k- 核心分解问题的一个子问题。 在本文中, 我们提出一个在 O(n log n) 时间运行的( 1 + epsilon) 近似解决方案, 通过取样与高度节点相邻的少数邻居, 从而解决密度图形输入大小的亚线性问题。 我们的算法也可以扩展为 k- 核心分解问题的 O(n log n) 时间解决方案。 这改进了 Bhattacharya et al. 的方法, 这种方法意味着( 4 + epsilon) 近似 ~ ~ O (n) 时间运行的降解性问题, 我们的技术类似于使用 K- 核心 和 降解性亚线性空间的其他绘图方法。 我们证明了我们的算法的理论保证, 并提供优化, 从而改进了我们实践中算法的运行时间。 大规模真实世界网络图的实验显示, 我们的算法比先前的计算方法要快得多, 包括2022 精确的变异算法。