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..
Scaling Fine-grained Modularity Clustering for Massive Graphs
Authors: Hiroaki Shiokawa, Toshiyuki Amagasa, Hiroyuki Kitagawa
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation In this section, we experimentally evaluate the efficiency and the clustering accuracy of g Scarf. We compared g Scarf with the following state-of-the-art graph clustering baseline algorithms: |
| Researcher Affiliation | Academia | Hiroaki Shiokawa , Toshiyuki Amagasa and Hiroyuki Kitagawa Center for Computational Sciences, University of Tsukuba, Japan EMAIL |
| Pseudocode | Yes | Algorithm 1 Proposed method: g Scarf |
| Open Source Code | No | The paper does not include a statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We used six real-world graphs published by SNAP [Leskovec and Krevl, 2014] and LAW [Boldi et al., 2011]. [...] In our experiments, we also used synthetic graphs with their ground-truth clusters generated by LFR-benchmark [Lancichinetti et al., 2009]. |
| Dataset Splits | No | The paper mentions using real-world graphs and LFR-benchmark graphs, but it does not specify any training, validation, or test splits. It evaluates clustering quality against ground-truth clusters directly. |
| Hardware Specification | Yes | All experiments were conducted on a Linux server with Intel Xeon E5-2690 CPU 2.60 GHz and 128 GB RAM. |
| Software Dependencies | No | The paper states that 'All algorithms were implemented in C/C++ as a single-threaded program' but does not specify any software libraries, frameworks, or their version numbers. |
| Experiment Setup | Yes | We set ϵ = 0.6 and µ = 5 as the same settings as used in [Chang et al., 2017]. [...] We used the recommended value θ = 0.06. |