Going Beyond Consistency: Target-oriented Multi-view Graph Neural Network
Authors: Sujia Huang, Lele Fu, Shuman Zhuang, Yide Qiu, Bo Huang, Zhen Cui, Tong Zhang
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three types of multi-view datasets validate the superiority of TGNN. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China 2School of Systems Science and Engineering, Sun Yat-Sen University, Guangzhou, China 3College of Computer and Data Science, Fuzhou University, Fuzhou, China 4School of Artificial Intelligence, Beijing Normal University, Beijing, China EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The algorithm of TGNN is presented in Appendix B. Algorithm 1 TGNN Algorithm Input: Multi-view graphs {Gv}V v=1, initial features X, Labels Y Output: Predicted labels Y^ 1: Initialize Φc, {Φsv}V v=1, Θ, Ω 2: for epoch = 1 to MaxEpoch do 3: for each view v in {1, ..., V} do 4: Obtain Hsv, Hcv using Eq. (2) 5: end for 6: Fuse {Hcv}V v=1 to obtain C using Eq. (3) 7: Calculate Lv sha using Eq. (4) 8: for each view v in {1, ..., V} do 9: Calculate Lv sep using Eq. (6) 10: Calculate Lce using Eq. (7) 11: end for 12: Calculate total loss L = P V v=1 (Lce + Lv sep) + αLsha + α P V v=1 Lv KL 13: Update Φc, {Φsv}V v=1, Θ, Ω by minimizing L 14: end for 15: Return Y^ |
| Open Source Code | Yes | Our code can refer to appendix. Complexity Analysis. ... Code and Appendix refer to https://github.com/huangsuj/TGNN.git. |
| Open Datasets | Yes | To evaluate the effectiveness of the proposed TGNN, we conduct comprehensive experiments on three types of multi-view datasets. These include three multi-relational datasets (ACM, DBLP, YELP), three multi-attribute datasets (Animals, HW, MNIST), and three multi-modal datasets (BDGP, esp-game, Flickr). A detailed description of these datasets is provided in Appendix C.1. |
| Dataset Splits | Yes | For node classification tasks on multi-relational graphs, 10% of the samples are used for validation, with the training set size varying across {20%, 40%}, and the remaining data is used for testing. For multi-attribute and multi-modal datasets, we split the data into training/testing/validation sets with a ratio of 10%/10%/80%. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or cloud platforms) are mentioned in the paper's experimental setup. |
| Software Dependencies | No | The paper mentions "Adam optimizer" but does not specify version numbers for any key software components or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The parameters of TGNN are configured as below: the training epoch is 300, learning rate is 0.001, the hidden dimension is 512, the number of layers is 2, θ and α range in {0.1, 0.5, 0.7, 1, 1.3} and {0.001, 0.005, 0.01, 0.05, 0.1, 0.5}, respectively. The Adam optimizer is adopted with a weight decay of 5e 6 for the DBLP, Flickr, and HW datasets, and 5e 4 for the remaining datasets. |