A Game Theoretic Approach For Core Resilience
Authors: Sourav Medya, Tiyani Ma, Arlei Silva, Ambuj Singh
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |