Majority Opinion Diffusion in Social Networks: An Adversarial Approach
Authors: Ahad N. Zehmakan5611-5619
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce and study a novel majority based opinion diffusion model. ... Our main purpose is to characterize classes of graphs where an attacker cannot succeed. In particular, we prove that if the maximum degree of the underlying graph is not too large or if it has strong expansion properties, then it is fairly resilient to such attacks. Furthermore, we prove tight bounds on the stabilization time of the process ... We also provide several hardness results for some optimization problems regarding stabilization time and choice of seed nodes. |
| Researcher Affiliation | Academia | Ahad N. Zehmakan Department of Computer Science, ETH Zurich abdolahad.noori@inf.ethz.ch |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical proofs and mathematical derivations. |
| Open Source Code | No | The paper does not provide any specific links to open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and analyzes abstract graph structures (e.g., star graph, complete graph, random regular graphs, Erdős-Rényi random graph) rather than using empirical datasets for training or evaluation. While 'SNAP (Leskovec and Krevl 2014)' is mentioned in the conclusion, it is for 'preliminary experiments' and not central to the main theoretical contributions, nor is access information provided in the context of specific experiments within the paper. |
| Dataset Splits | No | The paper is theoretical and does not report empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |