Learning Opinions in Social Networks
Authors: Vincent Conitzer, Debmalya Panigrahi, Hanrui Zhang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of learning opinions in social networks. The learner observes the states of some sample nodes from a social network, and tries to infer the states of other nodes, based on the structure of the network. We show that sampleefficient learning is impossible when the network exhibits strong noise, and give a polynomial-time algorithm for the problem with nearly optimal sample complexity when the network is sufficiently stable. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Duke University, Durham, USA. |
| Pseudocode | Yes | Algorithm 1: Learning Algorithm for General Networks; Algorithm 2: Empirical Risk Minimization in Networks |
| Open Source Code | No | The paper does not provide any explicit statements about releasing open-source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper does not specify the use of any publicly available datasets. It discusses 'm i.i.d. labeled samples' from a 'population distribution D' in a theoretical context without naming concrete datasets with access information. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups, therefore, it does not specify training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe concrete experimental setup details such as hyperparameters or training configurations. |