Graph Attention Retrospective
Authors: Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath
JMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our theoretical results on synthetic and real-world data. ... We provide an extensive set of experiments both on synthetic data and on three popular real-world datasets that validates our theoretical results. ... In this section, we demonstrate empirically our results on synthetic and real data. |
| Researcher Affiliation | Collaboration | Kimon Fountoulakis EMAIL David R. Cheriton School of Computer Science University of Waterloo Waterloo, Ontario, Canada. Amit Levi EMAIL Huawei Noah s Ark Lab Montreal, Quebec, Canada. |
| Pseudocode | No | The paper describes methods and algorithms using mathematical notation and textual explanations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology described, nor does it provide a link to a code repository. It mentions using PyTorch Geometric and OGB datasets, but not their own implementation code. |
| Open Datasets | Yes | We use popular real-world graph datasets Cora, Pub Med, and Cite Seer collected by Py Torch Geometric (Fey and Lenssen, 2019) and ogbn-arxiv from Open Graph Benchmark (Hu et al., 2020). |
| Dataset Splits | Yes | For real datasets, we use the default splits which come from Py Torch Geometric (Fey and Lenssen, 2019) and OGB (Hu et al., 2020). |
| Hardware Specification | No | The paper discusses experiments on synthetic and real data but does not provide any specific hardware details such as CPU/GPU models, memory, or specific computing platforms used. |
| Software Dependencies | No | The paper mentions using PyTorch Geometric and Open Graph Benchmark for datasets and their methods (MLP-GAT, GAT, GCN), but does not specify version numbers for any software libraries, frameworks, or programming languages used for implementation. |
| Experiment Setup | Yes | We set n = 1000, d = n/ log2(n), p = 0.5 and σ = 0.1. Results are averaged over 10 trials. ... For the original GAT architecture we fix w = µ/ µ and define the first head as a1 = 1/2(1, 1) and b1 = 1/2w T µ; The second head is defined as a2 = a1 and b2 = b1. ... For MLP-GAT we use the ansatz Ψ = (1p q 1p<q)Ψ where Ψ is given in (3) and (4) with R = 1. ... In the easy regime we fix the mean µ to be a vector where each coordinate is equal to 10σ p d. ... In the hard regime we fix the mean µ to a vector where each coordinate is equal to σ/ d. ... We fix p = 0.5 and vary q from log2(n)/n to 1 log2(n)/n. |