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..
Strategic Classification with Graph Neural Networks
Authors: Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on several real networked datasets demonstrate the utility of our approach. |
| Researcher Affiliation | Academia | Itay Eilat , Ben Finkelshtein , Chaim Baskin, Nir Rosenfeld Technion Israel Institute of Technology EMAIL EMAIL |
| Pseudocode | No | The paper describes computational steps for single and multiple rounds but does not present them in a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is publicly available at: http://github.com/Strategic GNNs/Code. |
| Open Datasets | Yes | We use three benchmark datasets used extensively in the GNN literature: Cora, Cite Seer, and Pub Med (Sen et al., 2008; Kipf & Welling, 2017), and adapt them to our setting. |
| Dataset Splits | Yes | All three datasets include a standard train-validation-test split, which we adopt for our use. For our purposes, we use make no distinction between train and validation , and use both sets for training purposes. ... In Table 2, the number of train samples is denoted ntrain, and the number of inductive test samples is denoted n test (all original transductive test sets include 1,000 samples). |
| Hardware Specification | No | The paper discusses the experimental setup and hyperparameters but does not mention specific hardware models like CPU or GPU types. |
| Software Dependencies | No | The paper mentions using 'Adam' for optimization but does not provide specific version numbers for any software, libraries, or dependencies. |
| Experiment Setup | Yes | We train using Adam and set hyperparameters according to Wu et al. (2019) (learning rate=0.2, weight decay=1.3 10 5). Training is stopped after 20 epochs (this usually suffices for convergence). Hyperparameters were determined based only on the train set: τ = 0.05, chosen to be the smallest value which retained stable training, and T = 3, as training typically saturates then (we also explore varying depths). We use β-scaled 2-norm costs, cβ(x, x ) = β x x 2, β R+, which induce a maximal moving distance of dβ = 2/β. We observed that values around d = 0.5 permit almost arbitrary movement; we therefore experiment in the range d [0, 0.5], but focus primarily on the mid-point d = 0.25 (note d = 0 implies no movement). Mean and standard errors are reported over five random initializations. |