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 | Archive PDF | Plain Text | 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. |