Polynomial-Delay MAG Listing with Novel Locally Complete Orientation Rules
Authors: Tian-Zuo Wang, Wen-Bo Du, Zhi-Hua Zhou
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the effectiveness and efficiency of the proposed MAG listing algorithm. Our method, MAGLIST-POLY, is compared with MAGLIST and BRUTEFORCE, which are presented by Wang et al. (2024a). ... The experimental results are shown in Fig. 9. When d < 10, all the methods return identical set of MAGs, demonstrating the effectiveness of MAGLIST-POLY. Moreover, MAGLIST-POLY consistently outperforms the other methods in runtime, highlighting its efficiency. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, China 2School of Artificial Intelligence, Nanjing University, China. Correspondence to: Zhi-Hua Zhou <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 MAGLIST-POLY Require: A PAG P 1: S = Record all the MAGs consistent with P 2: ORIENTGRAPH(P) 3: function ORIENTGRAPH(M, S) 4: if there are no circles in M then M is a MAG 5: S S {M} 6: else 7: Select a variable X where there are circles in M 8: I = Record all the PMGs without circles at X obtained from M 9: LOCALTRANSFORM(M, X) 10: for M in I do 11: ORIENTGRAPH(M , S) 12: end for 13: end if 14: end function 15: function LOCALTRANSFORM(H, X) 16: if there are no circles at X in H then 17: I I {H} 18: else 19: Select a vertex V with an edge X V in H 20: Obtain H from H by orienting X V and using the proposed orientation rules 21: LOCALTRANSFORM(H , X) 22: Obtain H from H by orienting X V and using the proposed orientation rules 23: LOCALTRANSFORM(H , X) 24: end if 25: end function Ensure: S |
| Open Source Code | No | The text does not contain a concrete statement about open-sourcing the code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | Finally, we conduct an experiment using real data processed from Wang et al. (2017) consisting of 7466 measurements of the abundance of phosphoproteins and phospholipids recorded under various experimental conditions. |
| Dataset Splits | No | The paper describes generating synthetic graphs and using real data from a previous study (Wang et al., 2017), but does not provide specific training/test/validation dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper describes parameters for synthetic data generation (number of vertices d and graph density ρ) and the procedure for real data experiments (2 interventions, repeated 10 times), but does not provide specific hyperparameters or system-level training settings for a machine learning model. |