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..
Adaptive Universal Generalized PageRank Graph Neural Network
Authors: Eli Chien, Jianhao Peng, Pan Li, Olgica Milenkovic
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also compare the performance of our GNN architecture with that of several state-of-the-art GNNs on the problem of node-classification, using well-known benchmark homophilic and heterophilic datasets. The results demonstrate that GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data. |
| Researcher Affiliation | Academia | Eli Chien & Jianhao Peng Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign, USA EMAIL Pan Li Department of Computer Science Purdue University, USA EMAIL Olgica Milenkovic Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign, USA EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available online.1 1https://github.com/jianhao2016/GPRGNN |
| Open Datasets | Yes | We use 5 homophilic benchmark datasets available from the Pytorch Geometric library, including the citation graphs Cora, Cite Seer, Pub Med (Sen et al., 2008; Yang et al., 2016) and the Amazon co-purchase graphs Computers and Photo (Mc Auley et al., 2015; Shchur et al., 2018). We also use 5 heterophilic benchmark datasets tested in Pei et al. (2019), including Wikipedia graphs Chameleon and Squirrel, the Actor co-occurrence graph, and webpage graphs Texas and Cornell from Web KB3. |
| Dataset Splits | Yes | We consider two different choices for the random split into training/validation/test samples, which we call sparse splitting (2.5%/2.5%/95%) and dense splitting (60%/20%/20%), respectively. |
| Hardware Specification | Yes | All experiments are performed on a Linux Machine with 48 cores, 376GB of RAM, and a NVIDIA Tesla P100 GPU with 12GB of GPU memory. |
| Software Dependencies | No | For all architectures, we use the corresponding Pytorch Geometric library implementations (Fey & Lenssen, 2019). ... All models use the Adam optimizer Kingma & Ba (2014). Specific version numbers for software components like Python or PyTorch are not provided. |
| Experiment Setup | Yes | We choose random walk path lengths with K = 10 and use a 2-layer (MLP) with 64 hidden units for the NN component. For the GPR weights, we use different initializations including PPR with α {0.1, 0.2, 0.5, 0.9}, γk = δ0k or δKk and the default random initialization in pytorch. Similarly, for APPNP we search the optimal α within {0.1, 0.2, 0.5, 0.9}. For other hyperparameter tuning, we optimize the learning rate over {0.002, 0.01, 0.05} and weight decay {0.0, 0.0005} for all models. ... We use early stopping 200 and a maximum number of epochs equal to 1000... For GCN, we use 2 GCN layers with 64 hidden units. For GAT, we use 2 GAT convolutional layers, where the first layer has 8 attention heads and each head has 8 hidden units; the second layer has 1 attention head and 64 hidden units. For GCN-Cheby, we use 2 steps propagation for each layer with 32 hidden units. ... For JK-Net, we use the GCN-based model with 2 layers and 16 hidden units in each layer. As for the layer aggregation part, we use a LSTM with 16 channels and 4 layers. For the MLP, we choose a 2-layer fully connected network with 64 hidden units. For APPNP we use the same 2-layer MLP with 10 steps of propagation. Besides the GPR-GNN, we fix the dropout rate for the NN part to be 0.5 as APPNP and optimize the dropout rate for the GPR part among {0, 0.5, 0.7}. |