Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment
Abstract
We consider the problem of resolving the envy of a given initial allocation by adding elements from a pool of goods. We give a characterization of the instances where envy can be resolved by adding an arbitrary number of copies of the items in the pool. From this characterization, we derive a polynomial-time algorithm returning a respective solution if it exists. If the number of copies or the total number of added items are bounded, the problem becomes computationally intractable even in various restricted cases. We perform a parameterized complexity analysis, focusing on the number of agents and the pool size as parameters. Notably, although not every instance admits an envy-free solution, our approach allows us to efficiently determine, in polynomial time, whether a solution exists—an aspect that is both theoretically interesting and far from trivial.
Cite
Text
Cai et al. "Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/417Markdown
[Cai et al. "Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/cai2024ijcai-dual/) doi:10.24963/ijcai.2024/417BibTeX
@inproceedings{cai2024ijcai-dual,
title = {{Dual Contrastive Graph-Level Clustering with Multiple Cluster Perspectives Alignment}},
author = {Cai, Jinyu and Zhang, Yunhe and Fan, Jicong and Du, Yali and Guo, Wenzhong},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {3770-3779},
doi = {10.24963/ijcai.2024/417},
url = {https://mlanthology.org/ijcai/2024/cai2024ijcai-dual/}
}