Universal Function Approximation on Graphs
Authors: Rickard Brüel Gabrielsson
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work we produce a framework for constructing universal function approximators on graph isomorphism classes. We prove how this framework comes with a collection of theoretically desirable properties and enables novel analysis. We show how this allows us to achieve state-of-the-art performance on four different well-known datasets in graph classification and separate classes of graphs that other graph-learning methods cannot. |
| Researcher Affiliation | Academia | Rickard Brüel-Gabrielsson rbg@cs.stanford.edu |
| Pseudocode | Yes | Algorithm 1 Subset Parsing Algorithm; Algorithm 2 Node Parsing Algorithm (NPA); Algorithm 3 Node Parsing Baseline Algorithm (NPBA) |
| Open Source Code | Yes | The complexity of the underlying algorithm is O(#edges #nodes) and code is publicly available1. 1https://github.com/bruel-gabrielsson/universal-function-approximation-on-graphs |
| Open Datasets | Yes | See Table 1 for results on graph classification benchmarks. ... Datasets: NCI1 MUTAG PROTEINS PTC |
| Dataset Splits | Yes | We report average and standard deviation of validation accuracies across the 10 folds within the cross-validation. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. It mentions using "Neural Networks" and "LSTM" but without version details. |
| Experiment Setup | Yes | In the experiments, the W(G) features are summed and passed to a classifier consisting of fully connected NNs. For NPA, sv sorts randomly, but with "-S", sv sorts based on the levels of subgraphs S1 and S2. For subgraph dropout "-D" we use K = 5. |