Proportional Selection in Networks
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Main Track. Pages 3514-3522.
https://doi.org/10.24963/ijcai.2026/391
Keywords
- Game Theory and Economic Paradigms: Computational social choice
Abstract
We address the problem of selecting k representative nodes from a network, aiming to simultaneously achieve two objectives: identifying the most influential nodes and ensuring that the selection proportionally reflects the diversity within the network. We propose a general approach to accomplish this by combining ideas from network science and computational social choice. Notably, our algorithms depend only on the connections between nodes and do not utilize any additional information that would explicitly identify groups of nodes. We analyze them theoretically, and demonstrate their effectiveness through a series of experiments.
@inproceedings{ijcai2026p391,
title = {Proportional Selection in Networks},
author = {Papasotiropoulos, Georgios and Skibski, Oskar and Skowron, Piotr and Wąs, Tomasz},
booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-26}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Diego Calvanese},
pages = {3514--3522},
year = {2026},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2026/391},
url = {https://doi.org/10.24963/ijcai.2026/391},
}
TY - CPAPER TI - Proportional Selection in Networks AU - Papasotiropoulos, Georgios AU - Skibski, Oskar AU - Skowron, Piotr AU - Wąs, Tomasz T2 - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2026 SP - 3514 EP - 3522 DO - 10.24963/ijcai.2026/391 UR - https://doi.org/10.24963/ijcai.2026/391 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: