Massively Parallel Single-Source SimRanks in O(log N) Rounds
Authors: Siqiang Luo, Zulun Zhu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we conduct a theoretical study on single-source Sim Rank computation in the Massive Parallel Computation (MPC) model, which is the standard theoretical framework modeling distributed systems. |
| Researcher Affiliation | Academia | Nanyang Technological University {siqiang.luo, ZULUN001}@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: Overall algorithm Algorithm 2: Detect meeting and calculate Sim Rank |
| Open Source Code | No | The paper mentions a technical report: "We leave the proofs of lemmas and theorems in the Appendix of our technique report [Luo and Zhu, 2024]". However, this is for a technical report, not an explicit statement or link to source code for the described methodology. |
| Open Datasets | No | The paper is a theoretical study focusing on algorithm design and analysis in the MPC model. It does not mention the use of any specific datasets, nor does it provide information about public availability or access to any training data. |
| Dataset Splits | No | The paper is a theoretical study and does not describe experiments on datasets. Therefore, it does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper focuses on the theoretical Massively Parallel Computation (MPC) model and does not describe any specific hardware (e.g., GPU models, CPU types, memory) used for running experiments. |
| Software Dependencies | No | The paper is a theoretical work focusing on algorithm design and complexity within the MPC model. It does not mention any specific software dependencies or their version numbers that would be required to reproduce experiments. |
| Experiment Setup | No | The paper is a theoretical study and does not describe an experimental setup with specific hyperparameters or system-level training settings. |