Diffusion and Auction on Graphs
Authors: Bin Li, Dong Hao, Dengji Zhao, Makoto Yokoo
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the first time, we expand the domain of the classic auction to a social graph and formally identify a new class of auction mechanisms on graphs. All mechanisms in this class are incentivecompatible and also promote all buyers to diffuse the auction information to others, whereby both the seller s revenue and the allocation efficiency are significantly improved comparing with the Vickrey auction. It is found that the recently proposed information diffusion mechanism is an extreme case with the lowest revenue in this new class. Our work could potentially inspire a new perspective for the efficient and optimal auction design and could be applied into the prevalent online social and economic networks. |
| Researcher Affiliation | Academia | 1University of Electronic Science and Technology of China, Chengdu, China 2Shanghai Tech University, Shanghai, China 3Kyushu University, Fukuoka, Japan |
| Pseudocode | Yes | Algorithm 1: Critical Diffusion Mechanism (CDM) Algorithm 2: The allocation policy of WDM Algorithm 3: The payment policy of WDM |
| Open Source Code | No | No statement about releasing source code or a link to a code repository is found in the paper. |
| Open Datasets | No | This paper is theoretical and does not use or reference any datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not describe experimental setups with dataset splits like training, validation, or testing. |
| Hardware Specification | No | This paper focuses on theoretical mechanisms and does not describe any experimental hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe any specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |