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..
Heterophily-Aware Personalized PageRank for Node Classification
Authors: Giuseppe Pirrò
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments validate our method s state-of-the-art performance across challenging heterophilous benchmarks. ... Section 7: Experimental Evaluation. ... Table 3: Node classification results; accuracy (Wiki-cooc, Roman-empire, Amazon-ratings, Squirrel-F, Chameleon-F) and ROC-AUC scores (Minesweeper, Tolokers, Questions, ar Xiv-year). Bold and underlined indicate best and second-best results. The two ablation studies compare different feature transformations (with fixed logistic regression classifier) and classifiers (with fixed feature transformation SGC with k=3). |
| Researcher Affiliation | Academia | Giuseppe Pirr o Department of Mathematics and Computer Science, University of Calabria 87046, Rende (CS), Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Heterophily-Aware Personalized Page Rank Input: 1: Graph G = (V, E) with node features X and pseudo-labels Y 2: Parameters: damping α (0, 1), local global restart β [0, 1], balance γ [0, 1] 3: max iter, convergence tolerance ϵ Output: 4: H-PPR score dictionary {πu : u V} 5: function H-PPR(G, X, Y, α, β, γ, max iter, ϵ) ... |
| Open Source Code | Yes | A more comprehensive discussion is available online1. 1https://github.com/giuseppepirro/happy |
| Open Datasets | Yes | We considered state-of-the-art hetherophilous datasets [Platonov et al., 2023b] ... These enhanced datasets are larger and cover a broader range of domains, as summarized in Table 2. 2https://github.com/yandex-research/heterophilous-graphs |
| Dataset Splits | Yes | We used the dataset splits provided by [Platonov et al., 2023b] and available online2 The authors fix 10 random 50%/25%/25% train/validation/test splits. |
| Hardware Specification | Yes | We ran experiments on a Mac Studio M2 Ultra with a 24-core CPU, 60-core GPU, and 32-core Neural Engine with 192GB of unified memory. |
| Software Dependencies | No | We implemented1 in Py Torch3 and MLX4 and integrated it into the evaluation pipeline provided by Platonov et al. [Platonov et al., 2023b]... Footnotes 3 and 4 link to pytorch.org and ml-explore/mlx respectively, but no specific version numbers for PyTorch or MLX are provided in the text. |
| Experiment Setup | Yes | We tuned random walk controls (α, β [0.1, 0.9]), computational settings (max iter [100, 1000], ϵ [10 6, 10 8]), and SGC iterations K (2-4). A detailed ablation analysis is discussed below. ... Ablation Study 1: Feature Transformation Analysis (fixed two-layer feed-forward-network used as classifier). SGC (k=2) ... SGC (k=4) ... GCN (l=2) ... GCN (l=3) ... GAT (l=2) ... GAT (l=3). Ablation Study 2: Classifier Analysis (fixed feature transformation via SGC with k=3). FFW3 ... Logistic ... SVM. |