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..
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Authors: Shubham Gupta, Sahil Manchanda, Sayan Ranu, Srikanta J. Bedathur
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluation: Extensive experiments on diverse real-world datasets establish that GRAFENNE consistently outperforms baseline methods across various levels of feature scarcity on both homophilic as well as heterophilic graphs. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, IIT Delhi. |
| Pseudocode | No | The paper describes the message passing layers using mathematical equations and text, but does not include a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our codebase is available at https://github.com/data-iitd/Grafenne. |
| Open Datasets | Yes | We evaluate GRAFENNE on the real-world graphs listed in Table 1. Among these, Actor is a heterophilic graph, whereas the rest are homophilic. Further details on the semantics of the datasets are provided in App. H. Table 1: Dataset statistics Cora (Sen et al., 2008) Cite Seer (Yang et al., 2016) Physics (Shchur et al., 2018) Actor (Pei et al., 2020) |
| Dataset Splits | Yes | We perform a 60% 20% 20% data split for train-test-validation. |
| Hardware Specification | Yes | All experiments are performed on an Intel Xeon Gold 6248 processor with 80 cores, 1 Tesla V-100 GPU card with 32GB GPU memory, and 377 GB RAM with Ubuntu 18.04. |
| Software Dependencies | No | The paper specifies the operating system (Ubuntu 18.04) but does not list specific versions for other key software components, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | We have used 2 layers of message-passing and trained GRAFENNE using the Adam optimizer with a learning rate of 0.0001 and choose the model based on the best validation loss. |