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..
Graph Component Contrastive Learning for Concept Relatedness Estimation
Authors: Yueen Ma, Zixing Song, Xuming Hu, Jingjing Li, Yifei Zhang, Irwin King
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results on three datasets show significant improvement over the state-of-the-art model. Detailed ablation studies demonstrate that our proposed approach can effectively capture the high-order relationship among concepts. We conduct comprehensive experiments with three different Transformer models on three datasets. |
| Researcher Affiliation | Academia | 1The Chinese University of Hong Kong, 2Tsinghua University |
| Pseudocode | No | The paper describes the proposed methods using textual explanations and mathematical equations, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available on Github1. 1Github: https://github.com/Panmani/GCCL |
| Open Datasets | Yes | We use the official dataset split for WORD whose train-test ratio is approximately 2:1. Since CNSE and CNSS do not provide an official dataset split, they are split randomly with a train-dev-test ratio of 7:2:1. |
| Dataset Splits | Yes | Since CNSE and CNSS do not provide an official dataset split, they are split randomly with a train-dev-test ratio of 7:2:1. |
| Hardware Specification | Yes | Experiments are conducted on four Nvidia TITAN V GPUs. |
| Software Dependencies | No | The paper mentions software components like 'Adam W optimizer' and specific 'Transformer models (BERT, RoBERTa, XLNet)', but does not provide version numbers for any libraries or programming languages. |
| Experiment Setup | Yes | We use the Adam W optimizer (Loshchilov and Hutter 2019) with learning rate = 1e-5 and ϵ = 1e-8, following a linear schedule. The Transformer models are trained for 5 epochs. For GC-NCE, we use α = 10. For Mo Co (He et al. 2020; Chen et al. 2020b), we use queue size Q = 32, momentum coefficient m = 1 - 1e-4, and temperature τ = 0.1. We use β = 0.1 for the overall loss. |