Identifying and Eliminating Majority Illusion in Social Networks
Authors: Umberto Grandi, Lawqueen Kanesh, Grzegorz Lisowski, Ramanujan Sridharan, Paolo Turrini
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we initiate the computational study of majority illusion in social networks, providing NPhardness and parametrised complexity results for its occurrence and elimination. |
| Researcher Affiliation | Academia | 1 University of Toulouse, France 2 IIT Jodhpur, India 3 University of Warwick, UK |
| Pseudocode | No | The paper describes algorithms conceptually, stating 'We next sketch this algorithm' and 'The details of this procedure can be found in the extended version of this paper', but it does not include structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. It links to 'shorturl.at/aisx B' for the extended version of the paper, not for code. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets, training, or public data availability. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving datasets or data splits for validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computations. |
| Software Dependencies | No | The paper describes theoretical algorithms and complexity analysis but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus there are no details about experimental setup or hyperparameters. |