A common way of partitioning graphs is through minimum cuts. One drawback of
classical minimum cut methods is that they tend to produce small groups, which
is why more balanced variants such as normalized and ratio cuts have seen more
success. However, we believe that with these variants, the balance constraints
can be too restrictive for some applications like for clustering of imbalanced
datasets, while not being restrictive enough for when searching for perfectly
balanced partitions. Here, we propose a new graph cut algorithm for
partitioning graphs under arbitrary size constraints. We formulate the graph
cut problem as a regularized Gromov-Wasserstein problem. We then propose to
solve it using accelerated proximal GD algorithm which has global convergence
guarantees, results in sparse solutions and only incurs an additional ratio of
$\mathcal{O}(\log(n))$ compared to the classical spectral clustering algorithm
but was seen to be more efficient.

通过提出一种基于正则化 Gromov-Wasserstein 问题的加速近端梯度下降算法，我们解决了图切割问题，并实现了稀疏解，相较于经典谱聚类算法，额外的复杂度仅为 O (log (n))，但效率更高。

通过最优输运实现任意尺寸约束的图割

Graph Cuts with Arbitrary Size Constraints Through Optimal Transport

This paper presents a framework for computing the Gromov-Wasserstein problem
between two sets of points in low dimensional spaces, where the discrepancy is
the squared Euclidean norm. The Gromov-Wasserstein problem is a generalization
of the optimal transport problem that finds the assignment between two sets
preserving pairwise distances as much as possible. This can be used to quantify
the similarity between two formations or shapes, a common problem in AI and
machine learning. The problem can be formulated as a Quadratic Assignment
Problem (QAP), which is in general computationally intractable even for small
problems. Our framework addresses this challenge by reformulating the QAP as an
optimization problem with a low-dimensional domain, leveraging the fact that
the problem can be expressed as a concave quadratic optimization problem with
low rank. The method scales well with the number of points, and it can be used
to find the global solution for large-scale problems with thousands of points.
We compare the computational complexity of our approach with state-of-the-art
methods on synthetic problems and apply it to a near-symmetrical problem which
is of particular interest in computational biology.

提出了一种计算低维空间中两组点之间 Gromov-Wasserstein 问题的框架，通过将 Quadratic Assignment Problem (QAP) 重新表述为低维域的优化问题来解决计算复杂度的挑战。该方法适用于具有成千上万个点的大规模问题，可用于找到全局解，并在合成问题和计算生物学中的一个感兴趣的问题上与最先进的方法进行比较。