This paper studies the NP-completeness and provides a polynomial-time algorithm for the disjoint separators problem in planar graphs when all sizes are equal to one.

This paper proposes a framework using building morphology from satellite imagery for fine-scale urban affluence mapping in Indian cities.

The paper shows that for non-planar orthogonal drawings of a graph, two drawings are sufficient instead of the planar case's three.

The paper proves XNLP-hardness of Directed Edge Geography and Undirected Edge Geography when parameterized by pathwidth, and shows their fixed-parameter tractability when parameterized by treewidth an…

A new polynomial-time algorithm is presented for optimally coloring perfect graphs using graph-theoretic operations and iterative walk counting.

This paper analyzes the Node Package Manager (npm) dependency network, demonstrates its vulnerability to targeted attacks, and proposes a defense strategy consisting of Centrality-Based Node-Hardening…

This paper settles the complexity of three sketching problems in graphs and distributions.

The paper introduces SkillCenter, a large open skill library for AI agents, with over 216,000 skills from various sources, and presents an end-to-end framework for acquiring, filtering, generating, gr…

This paper introduces ZipLine, a system for integrative analysis of multivariate graphs through a unified predicate language and learning algorithm.

PlanarBench introduces a novel benchmark to test LLM spatial reasoning by requiring them to draw planar graphs as ASCII art from an edge list, finding that edge count is a stronger difficulty predicto…

This paper studies the maximum size of an independent set in the discrete simplex graph and provides new bounds using translated smaller graphs and finite rational linear systems.

This paper proves that the minimum edge-outerplanarity of a planar graph can be computed in polynomial time.

This paper proposes a framework for measuring stadium noise levels with spatially distributed acoustic measurements, criticizing the lack of standardization in current record-breaking claims.

This paper introduces DDC, a Dead-Direction Conditioner that keeps a deep network's optimization on the symmetry quotient by conditioning the optimizer's state in the orbit decomposition of a $G$-inva…

The paper introduces FootsiesGym, an open-source environment for learning in a two-player, zero-sum, imperfect-information game, providing a vectorized simulator for efficient analysis.

This paper introduces MeanFlowNFT, a method that applies reinforcement learning to MeanFlow generators to improve performance and consistency.