Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters
Authors: Takanori Maehara, Hoang NT
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we develop a theoretical framework for graph classification problems in the partial observation setting (i.e., subgraph samplings). Equipped with insights from graph limit theory, we propose a new graph classification model that works on a randomly sampled subgraph and a novel topology to characterize the representability of the model. Our theoretical framework contributes a theoretical validation of mini-batch learning on graphs and leads to new learning-theoretic results on generalization bounds as well as size-generalizability without assumptions on the input. |
| Researcher Affiliation | Collaboration | Takanori Maehara Facebook AI London, United Kingdom tmaehara@fb.com Hoang NT Tokyo Tech & RIKEN AIP Tokyo, Japan hoangnt@net.c.titech.ac.jp |
| Pseudocode | Yes | Algorithm 1 Randomized Benjamini Schramm GNN |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not report on experiments with specific datasets; therefore, no public dataset access information is provided. |
| Dataset Splits | No | This is a theoretical paper and does not report on experiments; therefore, no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | This is a theoretical paper and does not report on experiments; therefore, no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | This is a theoretical paper and does not report on experiments; therefore, no specific experimental setup details or hyperparameters are provided. |