Iterative Methods via Locally Evolving Set Process
Authors: Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh, Xingzhi Guo, Deqing Yang, Yanghua Xiao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results confirm the efficiency of this novel framework and show up to a hundredfold speedup over corresponding standard solvers on real-world graphs. and 6 Experiments We conduct experiments over 17 graphs to solve (3) and explore the local clustering task. |
| Researcher Affiliation | Collaboration | 1 the School of Data Science, Fudan University, 2 Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University 3 Department of Computer Science, Stony Brook University, 4 Samsung SAIT AI Lab. |
| Pseudocode | Yes | Algo. 1 PUSH(u, α, p, z), Algo. 2 APPR(α, ϵ, s, G) via FIFO Queue, Algo. 3 LOCSOR(α, ϵ, s, G, ω) via FIFO Queue, Algo. 4 LOCGD(α, ϵ, s, G) via FIFO Queue |
| Open Source Code | Yes | Our code is available at https: // github. com/ baojian/ Local CH . |
| Open Datasets | Yes | Following Leskovec et al. [34], we treat all 17 graphs as undirected with unit weights. and Table 3: Dataset Statistics |
| Dataset Splits | No | The paper does not explicitly mention training, validation, and test splits for the datasets in the context of model training. The experiments involve solving linear systems on graphs rather than typical machine learning model training with data splits. |
| Hardware Specification | Yes | For our experiment, we used a server powered by an Intel(R) Xeon(R) Gold 5218R CPU, which features 40 cores (80 threads). The system is equipped with 256 GB of RAM. |
| Software Dependencies | Yes | All methods are implemented in Python 3.10 with the numba library [33]. |
| Experiment Setup | Yes | To compare local solvers to their standard counterparts, we set α = 0.1, randomly select 50 nodes from each graph to serve as es in (3)... The range of ϵ is ϵ [ α 2(1+α)ds , 10 4/n]. and For the local ISTA method [13], the precision parameter is set to ˆϵ = 0.5 for all experiments. ... For LOCSOR, the parameter ω is calculated as 2(1 + α)/(1 + α)2. |