A Little Charity Guarantees Fair Connected Graph Partitioning

Authors: Ioannis Caragiannis, Evi Micha, Nisarg Shah4908-4916

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We prove a lower bound which shows that α-balancedness and α-proportionality cannot be guaranteed for any α < 2 and α < 2 1/n, respectively (Theorem 1). Next, for n {2, 3}, we show that this bound is tight and such partitions can be found in polynomial time (Theorems 2 and 3). For higher values of n, we provide three efficient algorithms which obtain generally incomparable approximation guarantees: one ensures (3 + O(n/m))-balancedness and 3-proportionality, another ensures 4-balancedness and 2-proportionality, and the final one ensures (2+O(n2/m))balancedness and (2 1/n + O(n2/m))-proportionality.
Researcher Affiliation Academia Ioannis Caragiannis,1 Evi Micha,2 Nisarg Shah2 1 Aarhus University 2University of Toronto
Pseudocode Yes Algorithm 1: 2-balancedness and 1.5-proportionality for n = 2... Algorithm 2: (3 + O(n/m))-balancedness and 3proportionality for n 2... Algorithm 3: 4-balancedness and 2-proportionality for n 2
Open Source Code No The paper does not provide any concrete access to source code, such as repository links or explicit statements about code availability.
Open Datasets No This paper is theoretical and does not use datasets. Therefore, it does not provide information about public dataset access for training.
Dataset Splits No This paper is theoretical and does not use datasets. Therefore, it does not provide information about dataset splits for validation.
Hardware Specification No This is a theoretical paper that does not involve running experiments on specific hardware. Therefore, no hardware specifications are mentioned.
Software Dependencies No This is a theoretical paper that does not involve empirical experiments requiring specific software dependencies with version numbers.
Experiment Setup No This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings.