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].
Network, Popularity and Social Cohesion: A Game-Theoretic Approach
Authors: Jiamou Liu, Ziheng Wei
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the heuristics on graphs of size n = 5, . . . , 18. For each n = 8, . . . , 18, we generate 105 Erd os-Reny ı random graphs of size n. As shown in Fig. 2(a), both heuristics achieve high accuracy. We further evaluate the heuristics on 8 real-world networks |
| Researcher Affiliation | Academia | Jiamou Liu, Ziheng Wei The University of Auckland Department of Computer Science Auckland, New Zealand EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Construction of H given G=(V, E) and k>2; Algorithm 2 AP: Given a network G = (V, E) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | For each n = 8, . . . , 18, we generate 105 Erd os-Reny ı random graphs of size n. We further evaluate the heuristics on 8 real-world networks: karate club ZA (Zachary 1977), dolphins DO (Lusseau et al. 2003), college football FT (Girvan and Newman 2002), Facebook FB, Enron email network EN (Leskovec et al. 2009), and three physics collaboration networks AS, CM and HE (Leskovec et al. 2007). |
| 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 | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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 | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |