Given a weighted undirected graph, a number of clusters k, and an exponent z, the goal in the (k, z)-clustering problem on graphs is to select k vertices as centers that minimize the sum of the distances raised to the power z of each vertex to its closest center. This problem includes the well-known k-median (z = 1) and k-means (z = 2) clustering problems. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact (k, z)-clustering solution in the induced shortest-path metric. Prior works by Bhattacharya, Costa, Garg, Lattanzi, and Parotsidis [FOCS 2024] and by Bhattacharya, Costa, and Farokhnejad [STOC 2025] consider the dynamic (k, z)-clustering problem for point sets in metric spaces. These algorithms support adversarial point insertions and deletions under a model with access to pairwise distances. This model differs significantly from the dynamic graph setting, where no oracle access is given to pairwise distances and a single edge update can affect many distances - making these approaches inefficient when applied to graphs. While efficient dynamic k-center approximation algorithms on graphs exist [Cruciani, Forster, Goranci, Nazari, and Skarlatos, SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic (k,z)-clustering problem. As the main result of this paper, we develop a randomized incremental (k, z)-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of Õ(k m^{1+o(1)} + k^{1+1/(λ)} m), where λ ≥ 1 is an arbitrary fixed constant. Our incremental algorithm also achieves an amortized update time of Õ(k n^o(1) + k^{1+1/(λ)}) and consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size Õ(k) with a total update time of m^{1+o(1)} (independent of the parameter k) over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant - a property that may be of independent interest. In the second stage, we maintain a constant-factor approximate (k,z)-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static (k,z)-clustering algorithm.

Given points from an arbitrary metric space and a sequence of point updates sent by an adversary, what is the minimum recourse per update (i.e., the minimum number of changes needed to the set of centers after an update), in order to maintain a constant-factor approximation to a k-clustering problem? This question has received attention in recent years under the name consistent clustering.

In this paper we give the first efficient algorithms for the k-center problem on dynamic graphs undergoing edge updates. In this problem, the goal is to partition the input into k sets by choosing k centers such that the maximum distance from any data point to its closest center is minimized. It is known that it is NP-hard to get a better than 2 approximation for this problem.

Designing approximate all-pairs distance oracles in the fully dynamic setting is one of the central problems in dynamic graph algorithms. Despite extensive research on this topic, the first result breaking the O(√n) barrier on the update time for any non-trivial approximation was introduced only recently by Forster, Goranci and Henzinger [SODA’21] who achieved m1/ρ+o(1) amortized update time with a O(log n)3ρ−2 factor in the approximation ratio, for any parameter ρ ≥ 1. In this paper, we give the first constant-stretch fully dynamic distance oracle with small polynomial update and query time. Prior work required either at least a poly-logarithmic approximation or much larger update time. Our result gives a more fine-grained trade-off between stretch and update time, for instance we can achieve constant stretch of O(1/ρ2)4/ρ in amortized update time Õ(nρ), and query time Õ(nρ/8) for any constant parameter 0 < ρ < 1. Our algorithm is randomized and assumes an oblivious adversary. A core technical idea underlying our construction is to design a black-box reduction from decremental approximate hub-labeling schemes to fully dynamic distance oracles, which may be of independent interest. We then apply this reduction repeatedly to an existing decremental algorithm to bootstrap our fully dynamic solution.

A polygon C is an intersecting polygon for a set O of objects in ℝ² if C intersects each object in O, where the polygon includes its interior. We study the problem of computing the minimum-perimeter intersecting polygon and the minimum-area convex intersecting polygon for a given set O of objects. We present an FPTAS for both problems for the case where O is a set of possibly intersecting convex polygons in the plane of total complexity n. Furthermore, we present an exact polynomial-time algorithm for the minimum-perimeter intersecting polygon for the case where O is a set of n possibly intersecting segments in the plane. So far, polynomial-time exact algorithms were only known for the minimum perimeter intersecting polygon of lines or of disjoint segments.