Pivot-based Maximal Biclique Enumeration
Authors: Aman Abidi, Rui Zhou, Lu Chen, Chengfei Liu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive performance study using real world datasets from a wide range of domains. The experimental results demonstrate that our algorithm is more scalable and outperforms all the existing algorithms across all datasets and can achieve a significant speedup against the previous algorithms. |
| Researcher Affiliation | Academia | Aman Abidi , Rui Zhou , Lu Chen and Chengfei Liu Swinburne University of Technology, Melbourne, Australia {aabidi, rzhou, luchen, cliu}@swin.edu.au |
| Pseudocode | Yes | Algorithm 1: Enumerate all maximal bicliques |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described (e.g., a specific repository link or explicit code release statement). |
| Open Datasets | Yes | The datasets were obtained from KONECT repository [Kunegis, 2013]. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All the experiments were conducted on Eclipse IDE, deployed on the platform 64x Intel(R)Core(TM) i5-6400T with CPU frequency 2.20GHz and 8 GB RAM, running Windows 10 Enterprise operating system. |
| Software Dependencies | No | The paper mentions 'Eclipse IDE', 'Windows 10 Enterprise operating system', and 'implemented in Java' but does not provide specific version numbers for ancillary software like libraries or solvers that are crucial for reproducibility. |
| Experiment Setup | Yes | All the experiments were conducted on Eclipse IDE, deployed on the platform 64x Intel(R)Core(TM) i5-6400T with CPU frequency 2.20GHz and 8 GB RAM, running Windows 10 Enterprise operating system. The running time of each algorithm is averaged over 10, 7 or 5 runs for datasets that can be finished within 10 minutes, half an hour or one hour. The experiments were performed without using any kind of parallelism, i.e., single core was used. All the algorithms were implemented in Java. The threshold for a biclique is the number of minimum vertices in the two sets of a maximal biclique. p and q are the thresholds for each of the biclique vertex sets [Li et al., 2007]. |