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..
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Authors: Dasol Hwang, Jinyoung Park, Sunyoung Kwon, KyungMin Kim, Jung-Woo Ha, Hyunwoo J. Kim
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments demonstrate that the proposed method consistently improves the performance of link prediction and node classification on heterogeneous graphs. ... 4 Experiments |
| Researcher Affiliation | Collaboration | Dasol Hwang1 , Jinyoung Park1 , Sunyoung Kwon4 Kyung-Min Kim2,3 , Jung-Woo Ha2,3 , Hyunwoo J. Kim1 Korea University1, NAVER AI LAB2, NAVER CLOVA3, Pusan National University4 EMAIL EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Self-supervised Auxiliary Learning |
| Open Source Code | Yes | Our code is publicly available at https://github.com/mlvlab/SELAR. |
| Open Datasets | Yes | We use two public benchmark datasets from different domains for link prediction: Music dataset Last-FM and Book dataset Book-Crossing, released by KGNN-LS [52], Ripple Net [53]. We use two datasets for node classification: citation network datasets ACM and Movie dataset IMDB, used by HAN [46] for node classification tasks. |
| Dataset Splits | Yes | Algorithm 1 Self-supervised Auxiliary Learning Input: training data for primary/auxiliary tasks Dpr, Dau, mini-batch size Npr, Nau ... Dpr(train) m , Dpr(meta) m CVSplit(Dpr m , c) Split Data for CV ... We used 3-fold cross validation and the gradients of Θ w.r.t different meta-datasets are averaged to update Θk, see Algorithm 1. |
| Hardware Specification | No | Our experiments were mainly performed based on NAVER Smart Machine Learning platform (NSML) [54, 55]. This refers to a platform, not specific hardware components like GPU or CPU models. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | No | Implementation details are in the supplement. The main text does not provide specific hyperparameter values or detailed system-level training settings. |