Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Federated Node-Level Clustering Network with Cross-Subgraph Link Mending
Authors: Jingxin Liu, Renda Han, Wenxuan Tu, Haotian Wang, Junlong Wu, Jieren Cheng
ICML 2025 | Venue PDF | LLM Run Details | Input Tokens: 26,312 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,942 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of Fed NCN. Extensive experiments on five graph benchmark datasets demonstrate the effectiveness and superiority of the proposed Fed NCN compared to its competitors. |
| Researcher Affiliation | Academia | 1School of Cyberspace Security, Hainan University, Haikou, China 2School of Computer Science and Technology, Hainan University, Haikou, China. Correspondence to: Wenxuan Tu <EMAIL>, Jieren Cheng <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Training Procedure of Fed NCN Algorithm 2 Fed NCN for Client Algorithm Algorithm 3 Fed NCN for Server Algorithm |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Specifically, we use Cite Seer (Liu et al., 2023a), Pub Med (Jiang et al., 2024), Amazon-Computer, Amazon-Photo (Lin et al., 2021), and Questions (Platonov et al., 2024) as our experimental benchmark datasets. |
| Dataset Splits | Yes | Following the experimental setup from Fed TAD (Zhu et al., 2024), we construct distributed subgraphs by dividing the dataset into 5 clients, 10 clients, and 20 clients, respectively, where each client has a subgraph that is part of a complete graph. |
| Hardware Specification | Yes | All methods are implemented using Py Torch 2.4.0 and a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | Yes | All methods are implemented using Py Torch 2.4.0 and a single NVIDIA Ge Force RTX 4090 GPU. |
| Experiment Setup | Yes | We utilize a four-layer GNN on both the client and the server to obtain node embeddings, with hidden layer dimensions are 500-500-2000-10. Moreover, we use a one-layer MLP to obtain the local clustering signals, which are then uploaded to the server. During model optimization, we adopt the Adam optimizer (Xiao et al., 2024) with a learning rate of 1e-3. The client-server interaction is conducted 20 times, with the local model training 10 epochs during each interaction. |