This paper presents approximation algorithms for the one-way directed Reachability-Preserving Minimum Edge Cut problem in directed graphs.

The paper presents an algorithm for updating a Directed Minimum Spanning Tree using the weighted matroid intersection algorithm and a dynamic auxiliary graph.

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

The paper introduces GraphARC, a new benchmark for abstract reasoning on graph-structured data, demonstrating that current state-of-the-art language models struggle with full graph transformation task…

The paper introduces AxDafny, a verifier-guided repair framework that improves verification success in Dafny by generating implementations, invariants, assertions, and termination arguments.

This paper presents an optimized algorithm for computing cut sets of a path set in graph theory and introduces a vectorized computational framework for property calculations.

The paper investigates the relationship between graph classes, edit cost functions, and computational complexity of the graph edit distance, providing polynomial-time reductions and correspondences.

This paper introduces a new approach for scalable causal discovery in directed acyclic graphs (DAGs) with clustered data and local cluster-level effects.

This paper characterizes acyclic hypergraphs according to whether they admit unique canonical join trees as root and provides a linear-time algorithm to construct them.

This paper proposes a method for verifying functional correctness of message-passing concurrent programs using Constrained Horn Clause (CHC) solving.

The paper introduces a quotient-DAG view to accurately estimate unordered slate propensities for off-policy evaluation, solving the nuisance variance and computational gap inherent in standard importa…

The paper proposes GRiD, a novel framework that uses a two-phase training strategy (supervised pre-training and RL fine-tuning) to discover complex, graph-like rules for knowledge graph reasoning, ove…

This paper systematically evaluates the consistency of popular causal discovery benchmarks against real-world scientific literature, revealing significant variability in their accuracy.