Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Uncovering the Largest Community in Social Networks at Scale
Authors: Shohei Matsugu, Yasuhiro Fujiwara, Hiroaki Shiokawa
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation We experimentally evaluated the efficiency of Bn M. |
| Researcher Affiliation | Collaboration | Shohei Matsugu1 , Yasuhiro Fujiwara2 and Hiroaki Shiokawa3 1Graduate School of Science and Technology, University of Tsukuba, Japan 2NTT Communication Science Laboratories, Japan 3Center for Computational Sciences, University of Tsukuba, Japan |
| Pseudocode | Yes | Algorithm 1 Node-merging algorithm... Algorithm 2 Proposed method: Branch-and-Merge (Bn M) |
| Open Source Code | No | The paper does not provide a specific link or explicit statement about the open-source availability of the code for the described methodology. |
| Open Datasets | Yes | We tested 11 real-world social networks used in previous works [Gao et al., 2018; Zhou et al., 2021; Jiang et al., 2021; Chang et al., 2022] with more than 100,000 nodes, which are originally published by the Network Repository [Rossi and Ahmed, 2015]2. 2All graphs are publicly available online from https://networkrepository.com. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts) or reference standard predefined splits. |
| Hardware Specification | Yes | All experiments were conducted on a Linux server with Intel Xeon Gold 6246R CPU 3.40 GHz and 128 Gi B RAM. |
| Software Dependencies | No | The paper mentions "implemented in C/C++ using -O3 option" but does not specify versions for any ancillary software, libraries, or solvers. |
| Experiment Setup | Yes | All experiments were conducted on a Linux server with Intel Xeon Gold 6246R CPU 3.40 GHz and 128 Gi B RAM. All algorithms were implemented in C/C++ using -O3 option as a single-threaded program with the entire graph held in the main memory. Similar to previous studies [Gao et al., 2018; Zhou et al., 2021], we varied k from 2 to 5. |