Welfare Maximization in Fractional Hedonic Games

Authors: Haris Aziz, Serge Gaspers, Joachim Gudmundsson, Julian Mestre, Hanjo Taubig

IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We consider the computational complexity of computing welfare maximizing partitions for fractional hedonic games a natural class of coalition formation games that can be succinctly represented by a graph. For such games, welfare maximizing partitions constitute desirable ways to cluster the vertices of the graph. We present both intractability results and approximation algorithms for computing welfare maximizing partitions.
Researcher Affiliation Academia Haris Aziz and Serge Gaspers NICTA and UNSW, Sydney, Australia {haris.aziz, serge.gaspers}@nicta.com.au Joachim Gudmundsson and Juli an Mestre University of Sydney, Sydney, Australia {joachim.gudmundsson, mestre}@sydney.edu.au Hanjo T aubig TU Munich, Munich, Germany taeubig@in.tum.de
Pseudocode No The paper describes algorithms in prose within sections like '4 Approximating utilitarian welfare' and '5 Approximating egalitarian welfare', but it does not include formally labeled pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any statement about making its source code available, nor does it provide any links to a code repository.
Open Datasets No The paper is theoretical and does not involve empirical training on datasets, therefore no information about publicly available training datasets is provided.
Dataset Splits No The paper focuses on theoretical analysis and algorithm design; thus, it does not involve data validation splits.
Hardware Specification No The paper focuses on theoretical proofs and algorithm design, and thus does not include details on specific hardware used for experiments.
Software Dependencies No The paper is theoretical and does not specify any software dependencies with version numbers.
Experiment Setup No The paper describes theoretical algorithms and complexity results; therefore, it does not include specific experimental setup details such as hyperparameters or training configurations.