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 [1].
Axioms for Graph Clustering Quality Functions
Authors: Twan van Laarhoven, Elena Marchiori
JMLR 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results of an experimental parameter dependence analysis showed the high flexibility of adaptive scale modularity. In Figure 2 we show which graphs give which outcomes for adaptive scale modularity with various parameter settings. |
| Researcher Affiliation | Academia | Twan van Laarhoven EMAIL Elena Marchiori EMAIL Institute for Computing and Information Sciences Radboud University Nijmegen Postbus 9010 6500 GL Nijmegen, The Netherlands |
| Pseudocode | No | The paper describes theoretical concepts, axioms, and mathematical derivations without presenting any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, a link to a code repository, or mention of code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper discusses theoretical graph models like 'ring of cliques' and 'two-clique network' for analysis (Section 6.2), but does not refer to any specific publicly available datasets with access information (links, DOIs, or citations). |
| Dataset Splits | No | The paper does not use any specific datasets that would require train/test/validation splits. The analysis is performed on theoretical graph structures and models. |
| Hardware Specification | No | The paper focuses on theoretical and mathematical analysis of graph clustering quality functions. There is no mention of specific hardware (e.g., CPU, GPU models, or cloud computing resources) used for any computations or simulations. |
| Software Dependencies | No | The paper does not specify any particular software or library names with version numbers that would be required to reproduce the theoretical analysis or parameter dependence study. |
| Experiment Setup | Yes | In Figure 2 we show which graphs give which outcomes for adaptive scale modularity with various parameter settings. The graph consists of two subgraphs with w internal weight each, connected by an edge with weigh b. Hence the volume of the total graph is 2w + 2b. ... Overall, the parameter M has the effect of determining the scale... On the other hand, the γ parameter controls the slope of the boundary... |