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..
Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyond
Authors: Oleg Platonov, Denis Kuznedelev, Artem Babenko, Liudmila Prokhorenkova
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we first characterize some existing graph datasets in terms of homophily and LI to see which structural patterns are currently covered. Then, we show that LI, despite being a very simple graph characteristic, much better agrees with GNN performance than homophily.5 |
| Researcher Affiliation | Collaboration | Oleg Platonov HSE University Yandex Research Denis Kuznedelev Yandex Research Skoltech Artem Babenko Yandex Research Liudmila Prokhorenkova Yandex Research |
| Pseudocode | No | The paper describes methods and theoretical concepts but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Implementations of all the graph measures discussed in the paper and examples of their usage are provided in this Colab notebook. |
| Open Datasets | Yes | cora, citeseer, and pubmed [6, 25, 38, 27, 46] are three classic paper citation network benchmarks. Ogbn-arxiv and Ogbn-products [14] are two datasets from the recently proposed Open Graph Benchmark. |
| Dataset Splits | Yes | For each synthetic graph, we create 10 random 50%/25%/25% train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper states 'Our models are implemented using Py Torch [31] and DGL [44]' but does not specify version numbers for these software dependencies or the programming language. |
| Experiment Setup | Yes | For all models, we add a two-layer MLP after every graph neighborhood aggregation layer and further augment all models with skip connections [11], layer normalization [2], and GELU activation functions [12]. For all models, we use two graph neighborhood aggregation layers and hidden dimension of 512. We use Adam [15] optimizer with a learning rate of 3 10 5 and train for 1000 steps, selecting the best step based on the validation set performance. We use a dropout probability of 0.2 during training. |