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..
HHAN: Comprehensive Infectious Disease Source Tracing via Heterogeneous Hypergraph Neural Network
Authors: Qiang He, Yunting Bao, Hui Fang, Yuting Lin, Hao Sun
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on three real-world datasets demonstrate that HHAN significantly outperforms other state-of-the-art methods in tackling the complex challenge of tracing infectious diseases in heterogeneous populations. |
| Researcher Affiliation | Academia | 1Northeastern University, Shenyang, China 2Research Institute for Interdisciplinary Sciences and Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics, China EMAIL, EMAIL, EMAIL, EMAIL,EMAIL |
| Pseudocode | No | The paper describes the HHAN model, its modules (Agent-Based Modeling Module and Heterogeneous Graph Neural Network Module), and experimental procedures using equations and descriptive text, but it does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology described is publicly available. |
| Open Datasets | Yes | ACM Hypertext Conference Dataset: it was collected during the 2009 ACM Hypertext Conference, where the Socio Patterns project deployed the Live Social Semantics application. ... (Isella et al. 2011). School Dataset: it corresponds to the contact and friendship relationships among students at a high school in Marseille, France, measured using various techniques in December 2013 (Mastrandrea, Fournet, and Barrat 2015). Hospital Dataset: it contains the contact network between patients and healthcare workers (HCWs) within a hospital ward in Lyon, France, from 1:00 PM on December 6, 2010, to 2:00 PM on December 10, 2010, and 46 HCWs and 29 patients are included (Vanhems et al. 2013). |
| Dataset Splits | Yes | Each generated dataset is randomly divided into training, validation, and test sets in an 8:1:1 ratio. |
| Hardware Specification | No | The paper describes experimental settings such as learning rates, optimizers, dropout rates, batch sizes, and epochs, but does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using a 'GAT layer' and an 'Adam W optimizer' but does not specify any software libraries (e.g., PyTorch, TensorFlow) or their version numbers that would be necessary to replicate the experiments. |
| Experiment Setup | Yes | The model is trained with a dynamically adjusted learning rate using a learning rate scheduler. A dropout rate of 0.4 is applied after each GAT layer to prevent overfitting, and the Adam W optimizer is used with a learning rate of 0.005 and a weight decay of 1 10 4. The learning rate scheduler reduces the learning rate by half if validation performance plateaus, with a minimum learning rate of 1 10 6. The batch size is set to 20, and training is conducted over 300 epochs to ensure sufficient learning and convergence of the model. |