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..
Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning
Authors: Zhixiang Shen, Shuo Wang, Zhao Kang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments against various baselines on different downstream tasks demonstrate its superior performance and robustness. Surprisingly, our unsupervised method even beats the sophisticated supervised approaches. |
| Researcher Affiliation | Academia | Zhixiang Shen , Shuo Wang , Zhao Kang School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: The optimization of Info MGF-RA; Algorithm 2: The optimization of Info MGF-LA |
| Open Source Code | Yes | The source code and datasets are available at https://github.com/zxlearningdeep/Info MGF. |
| Open Datasets | Yes | We conduct experiments on four real-world benchmark multiplex graph datasets, which consist of two citation networks (i.e., ACM [18] and DBLP [18]), one review network Yelp [35] and a large-scale citation network MAG [36]. |
| Dataset Splits | Yes | Table 5: Statistics of datasets. Columns for 'Training', 'Validation', 'Test' are provided with specific numbers for each dataset (e.g., ACM: 600 training, 300 validation, 2,125 test). |
| Hardware Specification | Yes | We implement all experiments on the platform with Py Torch 1.10.1 and DGL 0.9.1 using an Intel(R) Xeon(R) Platinum 8457C 20 v CPU and an L20 48GB GPU. |
| Software Dependencies | Yes | We implement all experiments on the platform with Py Torch 1.10.1 and DGL 0.9.1... |
| Experiment Setup | Yes | Our model is trained with the Adam optimizer, and Table 6 presents the hyper-parameter settings on all datasets. Here, E represents the number of epochs for training, and lr denotes the learning rate. The hidden-layer dimension dh and representation dimension d of graph encoder GCN are tuned from {32, 64, 128, 256}. The number of neighbors k for k NN is searched from {5, 10, 15, 20, 30}. The order of graph aggregation r and the number of layers L in GCN are set to 2 or 3... The probability ρ of random feature masking is set to 0.5 or 0, and the temperature parameter τc in contrastive loss is fixed at 0.2. For Info MGF-RA using random graph augmentation, the probability ρs of random edge dropping is fixed at 0.5. For Info MGF-LA with learnable generative graph augmentation, the generator s learning rate lrgen is fixed at 0.001, the temperature parameter τ in Gumbel-Max is set to 1, and the hyper-parameter λ controlling the minimization of mutual information is fine-tuned from {0.001, 0.01, 0.1, 1, 10}. |