Stability of Graph Scattering Transforms
Authors: Fernando Gama, Alejandro Ribeiro, Joan Bruna
NeurIPS 2019 | Conference 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 fgama@seas.upenn.edu Joan Bruna Courant Institute of Math. Sci. New York University New York, NY 10012 bruna@cims.nyu.edu Alejandro Ribeiro Dept. of Electrical and Systems Eng. University of Pennsylvania Philadelphia, PA 19104 aribeiro@seas.upenn.edu |
| 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. |