Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Reduction-Based Algorithm for the Clique Interdiction Problem
Authors: Chenghao Zhu, Yi Zhou, Haoyu Jiang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on 124 large realworld networks demonstrate the superior performance of RECIP and validate the effectiveness of the proposed reduction rules. |
| Researcher Affiliation | Academia | Chenghao Zhu , Yi Zhou , Haoyu Jiang University of Electronic Science and Technology of China EMAIL, EMAIL, First EMAIL |
| Pseudocode | Yes | Algorithm 1 RECIP Input: Graph G = (V, E), an integer k Output: θ(G, k) 1: G, k, lb, Cliques preprocess(G, k) 2: ans branc and cut(G, k, lb, Cliques) 3: return ans |
| Open Source Code | Yes | The source codes are publicly available 1. 1https://github.com/axs7385/RECIP |
| Open Datasets | Yes | Our experiments use 124 real-world networks sourced from the SNAP database and the Network Repository[Leskovec and Sosiˇc, 2014; Rossi and Ahmed, 2015] 2. The dataset covers a variety of categories, including social networks, technological networks, biological networks, and more. These graphs can be as large as 1.9 million vertices and 5 million edges. We leave a detailed description in the appended file. 2The dataset can be downloaded from https://lcs.ios.ac.cn/ caisw/graphs.html. |
| Dataset Splits | No | The paper investigates an optimization problem (Clique Interdiction Problem) on given graphs, not a machine learning task requiring train/test/validation splits. The 'budgets' mentioned (e.g., k = 0.005|V|) are parameters for the optimization problem itself, not data partitioning for model training or evaluation. |
| Hardware Specification | Yes | All experiments were conducted on a machine equipped with an AMD R9-7940HX CPU and 16GB of RAM, running Ubuntu 22.04. |
| Software Dependencies | Yes | The codes were written in C++ and compiled with GCC version 11.4.0 using the -O2 optimization flag. The IBM CPLEX solver version 12.7 was used as the underlying solver for the integer linear program. |
| Experiment Setup | Yes | We compare the performance of the two algorithms under four different budget values (k { 0.005|V | , 0.01|V | , 0.02|V | , 0.05|V | }) with a runtime limit of 600 seconds. |