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..
A Game Theoretic Approach For Core Resilience
Authors: Sourav Medya, Tiyani Ma, Arlei Silva, Ambuj Singh
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the proposed algorithm outperforms competing solutions in terms of k-core minimization while being able to handle large graphs. Moreover, we illustrate how KCM can be applied in the analysis of the kcore resilience of networks. |
| Researcher Affiliation | Academia | Sourav Medya1 , Tiyani Ma2 , Arlei Silva3 and Ambuj Singh3 1Northwestern University 2University of California Los Angeles 3University of California Santa Barbara |
| Pseudocode | Yes | Algorithm 1: Greedy Cut (GC) Input: G, k, b Output: B: Set of edges to delete; Algorithm 2: Shapley Value Based Cut (SV) Input: G, k, b, Γ Output: B: Set of edges to delete |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The real datasets are available online and are mostly from SNAP1. The Facebook dataset is from [Viswanath et al., 2009]. Table 1 shows dataset statistics, including the largest k-core (a.k.a. degeneracy). We also apply a random graph (ER) generated using the Erdos-Renyi model. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All the experiments were conducted on a 2.59 GHz Intel Core i7-4720HQ machine with 16 GB RAM running Windows 10. |
| Software Dependencies | No | The paper states 'Algorithms were implemented in Java.' but does not provide specific version numbers for Java or any other software dependencies like libraries or frameworks. |
| Experiment Setup | Yes | Default parameters: We set the candidate edge set Γ to those edges (Mk(G)) between vertices in the k-core Ck(G). Unless stated otherwise, the value of the approximation parameter for SV (ϵ) is 0.05 and the number of samples is (ℓ+1) log |Γ|/2ϵ2. |