Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Authors: Song Wang, Yushun Dong, Xiao Huang, Chen Chen, Jundong Li
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four prevalent few-shot graph classification datasets demonstrate the superiority of FAITH over other state-of-the-art baselines. |
| Researcher Affiliation | Academia | 1University of Virginia 2Hong Kong Polytechnic University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes and data are available at https://github.com/Song W-SW/FAITH. |
| Open Datasets | Yes | We follow the work of [Chauhan et al., 2020] to evaluate our framework on four processed graph classification datasets, Letter-high, ENZYMES, TRIANGLES and Reddit-12K. ... Codes and data are available at https://github.com/Song W-SW/FAITH. |
| Dataset Splits | Yes | We follow the setting of [Chauhan et al., 2020] to split the classes in each dataset into training classes Yt and test classes Yf. We specify K {5, 10} and Q = 10, where K is the number of labeled graph samples for each class, and Q is the number of unlabeled graph samples in each task. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models) used for running experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch, Adam, GCN, and GIN, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The dimension of GCN [Kipf and Welling, 2017] used in the hierarchical task graph is set as Ds = Dp = Dt = 300. We utilize a 5-layer GIN [Xu et al., 2019] with the hidden dimension D = 128 as the embedding model GNNe. For the model optimization, we adopt Adam [Kingma and Ba, 2015] with a learning rate of 0.001, a dropout rate of 0.5, and the loss weight α = 1. |