Signed Laplacian Graph Neural Networks
Authors: Yu Li, Meng Qu, Jian Tang, Yi Chang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that SLGNN outperforms various competitive baselines and achieves state-of-the-art performance. |
| Researcher Affiliation | Academia | Yu Li1,8*, Meng Qu2,3, Jian Tang2,4,5 , Yi Chang6,7,8 1 College of Computer Science and Technology, Jilin University, China 2 Mila Qu ebec AI Institute, Canada 3 Univesit e de Montr eal, Canada 4 HEC Montr eal, Canada 5 CIFAR AI Research Chair, Canada 6 School of Artificial Intelligence, Jilin University, China 7 International Center of Future Science, Jilin University, China 8 Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Ministry of Education, China |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | We evaluate SLGNN on four popular signed graphs: Bitcoin Alpha and Bitcoin OTC are who-trusts-whom networks of people who trade on Bitcoin platforms and tag the others trust or distrust. Slashdot is a friendship network of people who tag each other as friends or foes on Slashdot technology-related news website. Epinions is a who-trust-whom network of people give trust or distrust tags on Epinions consumer review site. |
| Dataset Splits | No | For each signed graph, we randomly select 20% of the positive and negative links as the test set, while ensuring that the residual signed graph is still connected and used as the training set. The paper specifies a train/test split but does not explicitly mention a separate validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | For our proposed method SLGNN, we set the numbers of self-gating mechanism to M = 4 for Bitcoin Alpha, Slashdot and Epinions, and M = 2 for Bitcoin OTC, and employ 2 message aggregation layers, with a node representation dropout rate of 0.5, a link coefficient dropout rate of 0.5, and the hidden representation dimension of 64. We use Ada Grad (Duchi, Hazan, and Singer 2011) to optimize SLGNN with a learning rate of 0.01, a weight decay of 0.001. |