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..
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Authors: Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments on several public graph datasets and design an application of zero-shot cross-domain recommendation. The quantitative results demonstrate both the effectiveness and generalization of KMF with the comparison of state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China , 2State Key Laboratory of Cognitive Intelligence, China , 3Fudan University , 4Tianjin University , 5Meituan-Dianping Group EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We conduct experiments on three public real-world scholar datasets, DBLP, M10 [Pan et al., 2016], and Cora E (Cora Enrich) [Bojchevski and G unnemann, ]. |
| Dataset Splits | Yes | Class Split II: the seen classes are partitioned into train and validation parts, and the unseen classes are still used for testing. The [ train/val/test ] class split for Cora E, M10, and DBLP are [2/2/3], [3/3/4], and [2/2/2]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions tools like 'hyperopt' and 'BERT' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The embedding size of ei produced by BERT is 1,024. The radius of topic neighbors R = {0, 1, 2, 3}. We set the layer L = [0, 1, ..., 4] of GCN for compositional embedding, the dimension of hidden state and output db = 1024, the maximum of N(v) is set to 10, and the sampling number Q = [0, 5, 10, 15, 20] of negative contrastive pairs in graph contrastive learning. The magnitude parameters pm, pτ uniform[0.1, 0.5] in the topic mask sampling. In the geometric constraints, dr, rτ uniform[0.1, 0.5] as well. For other hyperparameters, we set the attenuation coefficient α = 0.8, temperature τ = 10, filtering soft cut %P = 25%, and tradeoff λ1,2 uniform[0.1, 0.5] according to the feedback of experimental performance. We adopt Adam [Kingma and Ba, 2015] with a learning rate of 0.001 to optimize our model. |