arXiv:2606. 03923v1 Announce Type: new Abstract: Graph coloring seeks to assigns colors to a graph's nodes so that adjacent nodes receive different colors, using as few colors as possible.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper investigates how continuous random node features affect the expressivity of message‑passing neural networks (MPNNs). It introduces a population‑level framework for size‑uniform approximation, showing that continuous random features can be quantized to finitely many colors with negligible error, allowing transfer of finite‑color distinguishability limits to size‑uniform approximation. The authors prove that such MPNNs are not size‑uniformly universal, analyze expressivity gains from colors and independent trials, establish incomparability with higher‑order Weisfeiler‑Leman methods, and provide finite‑color MPNN constructions for tree subgraph isomorphism and a local minimum spanning tree task.
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
arXiv:2608. 14999v1 Announce Type: cross Abstract: Constructing special graphs is an important task within graph theory and computer science.
arXiv:2602.13106v2 Announce Type: replace-cross Abstract: In recent years, there has been growing interest in understanding neural architectures' ability to learn to execute discrete algorithms, a li...
arXiv:2608.30103v1 Announce Type: cross Abstract: Bilevel mixed-integer linear optimization problems model hierarchical decision processes in which a leader anticipates the optimal response of a foll...
arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.