Modularity aided consistent attributed graph clustering via coarsening
Authors: Yukti Makhija, Samarth Bhatia, Manoj Kumar, Sandeep Kumar
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets demonstrate its superiority over existing state-of-the-art methods for both attributed and non-attributed graphs. 5 Experiments |
| Researcher Affiliation | Academia | Samarth Bhatia* EMAIL Indian Institute of Technology, Delhi Yukti Makhija* EMAIL Indian Institute of Technology, Delhi Manoj Kumar EMAIL LNM Institute of Technology, Jaipur Sandeep Kumar EMAIL Indian Institute of Technology, Delhi |
| Pseudocode | Yes | Algorithm 1 Q-MAGC Algorithm Require: G(X, Θ), α, β, γ, λ 1: t 0 2: while Stopping Criteria not met do 3: Update Ct+1 as in Equation 11 4: Update Xt+1 C as in Equation 14 5: t t + 1 6: end while 7: return Ct, Xt C |
| Open Source Code | Yes | A Implementation The implementations for all the experiments can be found at https://github.com/plutonium-239/MAGC. |
| Open Datasets | Yes | We evaluate our method on a diverse set of datasets, including small attributed datasets like Cora and Cite Seer, larger datasets like Pub Med, and unattributed datasets such as Airports (Brazil, Europe, and USA). A summary of these can be seen in Table 1. Additionally, we test our method on very large datasets like Coauthor CS/Physics, Amazon Photo/PC, and ogbn-arxiv. A detailed summary of all the datasets used is provided in Appendix J. We use datasets directly from the pytorch_geometric package, so no preprocessing is needed. |
| Dataset Splits | No | The paper mentions "full-batch training" and "batching" for certain large graphs like ogbn-arxiv, but it does not specify explicit train/validation/test split percentages or detailed methodologies for data partitioning across the datasets used in the experiments. |
| Hardware Specification | Yes | All experiments were run on an NVIDIA A100 GPU and Intel Xeon 2680 CPUs. |
| Software Dependencies | Yes | All experiments used the same environment running Cent OS 7, Python 3.9.12, Py Torch 2.0, Py Torch Geometric 2.2.0. |
| Experiment Setup | Yes | Learning Rate: [0.001, 0.1] α: [500, 10000] β: [10, 250] γ: [100, 1000] λ: [0, 100] λrecon: [10, 250] λkl: [0.001, 0.1]. All experiments were run on an NVIDIA A100 GPU and Intel Xeon 2680 CPUs. We are usually running 4-16 experiments together to utilize resources (for example, in 40GB GPU memory, we can run 8 experiments on Pub Med simultaneously). |