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..
Faster Local Solvers for Graph Diffusion Equations
Authors: Jiahe Bai, Baojian Zhou, Deqing Yang, Yanghua Xiao
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on 18 graphs ranging from small-scale (cora) to large-scale (papers100M), mainly collected from Stanford SNAP [45] and OGB [42] (see details in Table 3). We focus on the following tasks: 1) approximating diffusion vectors f with fixed stop conditions using both CPU and GPU implementations; 2) approximating dynamic PPR using local methods and training dynamic GNN models based on Instant GNN models [75]. |
| Researcher Affiliation | Academia | Jiahe Bai 1 Baojian Zhou 1,2 Deqing Yang 1,2 Yanghua Xiao 2 1 the School of Data Science, Fudan University, 2 Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University jhbai20,bjzhou,yangdeqing,EMAIL |
| Pseudocode | Yes | Algo 1 APPR(G, ϵ, α, s) [2] adopted from [8] 1: x 0, r αes 2: while u, ru ϵαdu do 3: r, x PUSH(u, r, x) 4: Return x 1: PUSH(u, r, x): 2: ru ru 3: xu xu + ru 4: ru 0 5: for v N(u) do 6: rv rv + (1 α) ru du 7: Return (r, x) |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Jiahe Bai/Faster-Local-Solver-for-GDEs. |
| Open Datasets | Yes | We conduct experiments on 18 graphs ranging from small-scale (cora) to large-scale (papers100M), mainly collected from Stanford SNAP [45] and OGB [42] (see details in Table 3). |
| Dataset Splits | No | We randomly sample 50 nodes uniformly from lower to higher-degree nodes for all experiments. All results are averaged over these 50 nodes. |
| Hardware Specification | Yes | We conduct experiments using Python 3.10 with Cu Py and Numba on a server with 80 cores, 256GB of memory, and two 28GB NVIDIA-4090 GPUs. |
| Software Dependencies | Yes | We conduct experiments using Python 3.10 with Cu Py and Numba on a server with 80 cores, 256GB of memory, and two 28GB NVIDIA-4090 GPUs. |
| Experiment Setup | Yes | We set (αPPR = 0.1, ϵ = 1/n) for PPR and (αKatz = 1/( A 2 + 1), ϵ = 1/m) for Katz. We use a temperature of τ = 10 and ϵ = 1/ n for HK. All algorithms for HK estimate the number of iterations by considering the truncated error of Taylor approximation. Table 2 presents the speedup ratio of four local methods over their standard counterparts. |