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..
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Authors: Ziang Chen, Jialin Liu, Xiaohan Chen, Wang, Wotao Yin
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A small-scale numerical experiment is conducted to directly validate our theoretical findings. We implement numerical experiments to validate our theoretical findings in Section 4. |
| Researcher Affiliation | Collaboration | Ziang Chen Massachusetts Institute of Technology EMAIL; Jialin Liu University of Central Florida EMAIL; Xiaohan Chen Xinshang Wang Wotao Yin Alibaba US, DAMO Academy EMAIL |
| Pseudocode | Yes | Algorithm 1 The WL test for MILP-Graphs; Algorithm 2 2-FWL test for MILP-Graphs |
| Open Source Code | Yes | We provide the source code and data to reproduce the main results, along with sufficient instructions to run the code, in the supplementary materials. |
| Open Datasets | No | We randomly generate 100 MILP instances, with 6 constraints and 20 variables... We train the MP-GNN and 2-FGNN to fit the SB scores of (4.1) and (4.2)... |
| Dataset Splits | No | The paper mentions training data and training loss ('average ℓ2 norm of errors across all training instances') but does not explicitly state a validation set or split for hyperparameter tuning. |
| Hardware Specification | Yes | The numerical experiments are conducted on a single NVIDIA Tesla V100 GPU |
| Software Dependencies | Yes | We implement MP-GNN and 2-FGNN with Python 3.6 and Tensor Flow 1.15.0 [1]. |
| Experiment Setup | Yes | We train an MP-GNN and a 2-FGNN with L = 2... p0, q0 are parameterized as linear transformations followed by a non-linear activation function; {pl, ql, f l, gl}L l=1 are parameterized as 3-layer multi-layer perceptrons (MLPs) with respective learnable parameters; and the output mapping r is parameterized as a 2-layer MLP. All layers map their input to a 1024-dimensional vector and use the Re LU activation function. ... minimizing 1 2 P G G Fθ(G) SB(G) 2 with respect to θ, using Adam [31]. ... with a learning rate of 10 5 for all networks. We decay the learning rate to 10 6 and 10 7 when the training error reaches 10 6 and 10 12 respectively. |