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..
BScNets: Block Simplicial Complex Neural Networks
Authors: Yuzhou Chen, Yulia R. Gel, H. Vincent Poor6333-6341
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments indicate that BSc Nets outperforms the state-of-the-art models by a significant margin while maintaining low computation costs. |
| Researcher Affiliation | Academia | Yuzhou Chen,1 Yulia R. Gel,2 H. Vincent Poor1 1Department of Electrical and Computer Engineering, Princeton University 2Department of Mathematical Sciences, University of Texas at Dallas |
| Pseudocode | No | The paper presents an architecture diagram (Figure 2) and describes the model components in text, but it does not include a formal pseudocode block or algorithm. |
| Open Source Code | Yes | Our datasets and codes are available on https://github.com/ BSc Nets/BSc Nets.git. |
| Open Datasets | Yes | We experiment on three types of networks (i) citation networks: Cora and Pub Med (Sen et al. 2008); (ii) social networks: (1) flight network: Airport (Chami et al. 2019), (2) criminal networks: Meetings and Phone Calls (Cavallaro et al. 2020), and (3) contact networks: High School network and Staff Community (Salath e et al. 2010; Eletreby et al. 2020); (iii) disease propagation tree: Disease (Chami et al. 2019). |
| Dataset Splits | Yes | Following (Chami et al. 2019), for all datasets, we randomly split edges into 85%/5%/10% for training, validation, and testing. |
| Hardware Specification | Yes | We implement our proposed BSc Nets with Pytorch framework on two NVIDIA RTX 3090 GPUs with 24 Gi B RAM. |
| Software Dependencies | No | The paper mentions implementing the model with the 'Pytorch framework' but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For all datasets, BSc Nets is trained by the Adam optimizer with the Cross Entropy Loss function. More details about the experimental setup and hyperparameters are in Appendix B.2. |