The paper introduces a new automated feature extraction method for algorithm selection in constraint optimisation, using graph conversion and Weisfeiler‑Lehman (WL) graph kernels to produce structural representations of problem instances. It proposes a cut‑based WL feature (“WLc”) that captures structural partitions and claims this representation improves predictive performance over existing tools like “fzn2feat”. Experiments on MiniZinc Challenge instances for Borda‑count maximisation and predictive‑accuracy maximisation show that SVMs with WLc features outperform “fzn2feat”, while Random Forests and MLPs yield comparable results.
By Alessio Pellegrino, Jacopo Mauro
arXiv:2508. 08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances.
By Xiang Li, Shanshan Wang, Chenglong Xiao
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
By Weishuo Ma, Yanbo Wang, Xiyuan Wang, Lei Zou, Muhan Zhang
The paper introduces an automated method that employs Large Language Models in a check–fix–verify loop to generate executable Python scripts for extracting problem-specific features from constraint satisfaction problems. Given a MiniZinc model and instance, the LLM agent produces code that builds a typed graph representation and computes structural properties such as graph density, variable clustering, and constraint tightness. Evaluated on vehicle routing, car sequencing, and fixed‑length error‑correcting codes, the synthesized extractors enable algorithm selectors that outperform expert‑curated mzn2feat features and transformer‑based trans2feat variants, while remaining interpretable.
By Hai Xia, Carlos Ans\'otegui, Stefan Szeider
The paper introduces EDiS, an Edge‑Disjoint Subgraph sparsification framework that separates a one‑time structural extraction of a graph into cacheable edge‑disjoint subgraphs from the per‑epoch graph composition. By recombining these cached subgraphs under edge‑budget constraints, EDiS avoids repeated sampling or recomputation while allowing varying training graphs across epochs. Experiments on 19 node‑classification benchmarks show that EDiS outperforms 17 baselines in accuracy/ROC‑AUC, achieving the lowest average rank and gap‑to‑best, especially at tight edge budgets.
By Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, Mahantesh M Halappanavar
arXiv:2603. 24304v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) deliver strong performance on graph tasks, but their accuracy drops significantly under out-of-distribution (OOD) scenarios.
By Bowen Lu, Liangqiang Yang, Teng Li, Kun Zhang