Strategic Classification with Graph Neural Networks
Authors: Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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 {itayeilat,benfin}@campus.technion.ac.il {chaimbaskin,nirr}@cs.technion.ac.il |
| 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. |