Network Formation under Random Attack and Probabilistic Spread
Authors: Yu Chen, Shahin Jabbari, Michael Kearns, Sanjeev Khanna, Jamie Morgenstern
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In our game, computing utilities or even verifying network equilibrium is computationally hard. We circumvent this difficulty by proving structural properties for equilibrium networks. ... We obtain these structural results even tough we cannot compute utilities nor even verify that an equilibrium has reached due to computational barriers. We view these results as are our most significant technical contributions. |
| Researcher Affiliation | Academia | 1University of Pennsylvania 2Georgia Tech {chenyu2, jabbari, mkearns, sanjeev}@cis.upenn.edu, jamiemmt.cs@gatech.edu |
| Pseudocode | No | The paper is theoretical and focuses on proofs and theorems. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is being released. It mentions 'The full technical version of this paper is available at http: //arxiv.org/abs/1906.00241.' which refers to the paper itself. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies, datasets, or training. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits or validation. |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies with specific version numbers relevant to experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |