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].
Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection
Authors: Pan Li, I Chien, Olgica Milenkovic
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real, large-scale networks illustrate the superiority of IPR compared to other GPRs for seeded community detection. |
| Researcher Affiliation | Academia | Pan Li UIUC EMAIL Eli Chien UIUC EMAIL Olgica Milenkovic UIUC EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | The second category includes three real world networks, Citeseer [44], Cora [45] and Pub Med [46], all frequently used to evaluate community detection algorithms [47, 48]. These networks comprise several non-overlapping communities, and may be roughly modeled as SBMs. The third category includes the Amazon (product) network and the DBLP (collaboration) network from the Stanford Network Analysis Project [49]. |
| 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. It mentions seed selection but not overall dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For synthetic networks, the parameter θ in IPR is set to λ2 = 0.05 q / (0.05+q), following the recommendations of Section 4.2. For real world networks, we avoid computing λ2 exactly and set θ {0.99, 0.95, 0.90}. The parameters of the PPR and HPR are chosen to satisfy α {0.9, 0.95} and h {5, 10} and to offer the best performance, as suggested in [50, 51, 14]. |