Stability of Graph Scattering Transforms
Authors: Fernando Gama, Alejandro Ribeiro, Joan Bruna
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show through numerical experiments in section 5, that the GST representation is not only stable, but also captures rich enough information. |
| Researcher Affiliation | Academia | Fernando Gama Dept. of Electrical and Systems Eng. University of Pennsylvania Philadelphia, PA 19104 EMAIL Joan Bruna Courant Institute of Math. Sci. New York University New York, NY 10012 EMAIL Alejandro Ribeiro Dept. of Electrical and Systems Eng. University of Pennsylvania Philadelphia, PA 19104 EMAIL |
| Pseudocode | No | The paper describes the architecture and computations mathematically but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Datasets and source code: http://github.com/alelab-upenn/graph-scattering-transforms |
| Open Datasets | Yes | For the second and third experiments, we consider two problems involving real-world data... authorship attribution and source localization over a Facebook subgraph, namely the same problems considered in [22]. The references [37], [38], and [39] point to the specific datasets and problems used. |
| Dataset Splits | No | The paper refers to using datasets for 'authorship attribution' and 'source localization' problems, noting they are 'in the same scenario considered in [22]'. However, this paper does not explicitly provide specific train/validation/test split percentages, sample counts, or a detailed splitting methodology for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or specific computing cluster types) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, or specific library versions) that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | We consider GSTs with 6 scales and 3 layers, yielding representations with 43 coefficients when using the monic cubic polynomial [31] and a tight Hann wavelet [32]. For the geometric scattering we consider the low pass operator to compute 4 moments, as used in [28], leading to 172 coefficients. |