Fast Online Node Labeling for Very Large Graphs
Authors: Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct extensive experiments on the online node classification for different-sized graphs and compare FASTONL with baselines. We address the following questions: 1) Do these parameterized kernels work and capture label smoothness?; 2) How does FASTONL compare in terms of classification accuracy and run time with baselines? |
| Researcher Affiliation | Collaboration | 1School of Data Science, Fudan University, Shanghai, China 2the Shanghai Key Laboratory of Data Science, Fudan University 3Department of Computer Science, Stony Brook University, Stony Brook, USA 4Samsung-SAIT AI lab, Montreal, Canada. |
| Pseudocode | Yes | Algorithm 1 RELAXATION(G, λ, D)(Rakhlin & Sridharan) |
| Open Source Code | Yes | Our code and datasets have been provided as supplementary material and are publicly available at https://github. com/baojian/Fast ONL. |
| Open Datasets | Yes | We collect ten graph datasets where nodes have true labels (Tab. 4) and create one large-scale Wikipedia graph where chronologically-order node labels are from ten categories of English Wikipedia articles. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits (e.g., percentages or specific counts) for reproducibility, nor does it specify cross-validation settings. It discusses parameters and sequential sample processing. |
| Hardware Specification | No | All experiments are conducted on a server with 40 cores and 250GB of memory. |
| Software Dependencies | No | We implemented all methods using Python and used the inverse function of scipy library to compute the matrix inverse. |
| Experiment Setup | Yes | All experimental setups, including parameter tuning, are further discussed in Appendix D. For FASTONL, we chose the first two kernels defined in Tab. 1 and named them as FASTONL-K1 and FASTONL-K2, respectively. |