Community-Aware Graph Transformer for Brain Disorder Identification
Authors: Shengbing Pei, Jiajun Ma, Zhao Lv, Chao Zhang, Jihong Guan
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results indicate that our proposed method significantly improves performance on both large and small datasets, and can reliably capture the interactions between sub-networks, demonstrating its generalization and interpretability. |
| Researcher Affiliation | Academia | 1Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University 2School of Computer Science and Technology, Tongji University EMAIL, EMAIL, EMAIL, iiphci EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods through text and diagrams (Figure 2 shows the overall framework) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | All codes are implemented using the Py Torch and Py G libraries. Experiments are conducted on a Windows server equipped with an Intel Core i7-10700 CPU (2.90 GHz), a Ge Force GTX 3080 Ti GPU and 32 GB of RAM. The code is available at https://github.com/null-cks/CAGT. |
| Open Datasets | Yes | ABIDE I is an open-source ASD diseases database comprising data from 17 international sites, available at https: //fcon 1000.projects.nitrc.org/indi/abide. The Preprocessed Connectomes Project (PCP) has preprocessed the f MRI data for each subject, resulting in rs-f MRI data for 1,035 subjects, including 505 ASD patients and 530 normal controls (NC). REST-MDD is a publicly available MDD diseases database comprising 25 study cohorts, accessible at http: //rfmri.org/REST-meta-MDD. The data were preprocessed using the Data Processing Assistant for Resting-State f MRI (DPARSF) toolbox [Yan et al., 2016]. Following the official recommendation to exclude overlapping data from site S4, the refined dataset consists of 2,380 rs-f MRI scans, including 1,276 MDD patients and 1,104 NC. Tao Wu and Neurocon datasets are among the earliest image datasets available for Parkinson s research. We followed the preprocessing steps outlined in [Liu et al., 2024b] using the DPARSF toolbox. |
| Dataset Splits | Yes | We evaluate the model s performance using ten-fold crossvalidation. |
| Hardware Specification | Yes | Experiments are conducted on a Windows server equipped with an Intel Core i7-10700 CPU (2.90 GHz), a Ge Force GTX 3080 Ti GPU and 32 GB of RAM. |
| Software Dependencies | No | All codes are implemented using the Py Torch and Py G libraries. The paper mentions the use of PyTorch and PyG libraries but does not specify their version numbers. |
| Experiment Setup | Yes | Key parameters included a batch size of 64, a total of 70 epochs, an initial learning rate of 1 10 4, and a weight decay of 1 10 6. The top-k connections for each node are retained as edges, with their values fixed at 30. The RWPE dimension S is set to 30. The Transformer architecture is configured with 2 layers and 8 heads. A dropout rate of 0.2 is applied to the GINE, Transformer, and the final fully connected classification layer. |