# Proportional Selection in Networks

arXiv: https://arxiv.org/abs/2502.03545 · HTML: https://mlsanity.com/paper/2502.03545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 76 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 30 of 33)

- [Multi-scale Attributed Node Embedding](https://mlsanity.com/paper/1909.13021/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Generative Social Choice](https://mlsanity.com/paper/2309.01291/) (2023)
- [Justified Representation in Approval-Based Committee Voting](https://mlsanity.com/paper/1407.8269/) (2014)
- [Blind Justice: Fairness with Encrypted Sensitive Attributes](https://mlsanity.com/paper/1806.03281/) (2018)
- [Multi-Winner Voting with Approval Preferences](https://mlsanity.com/paper/2007.01795/) (2020)
- [Proportional Participatory Budgeting with Additive Utilities](https://mlsanity.com/paper/2008.13276/) (2020)
- [Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency](https://mlsanity.com/paper/1404.0900/) (2014)
- [Proportional Justified Representation](https://mlsanity.com/paper/1611.09928/) (2016)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [Group-Fairness in Influence Maximization](https://mlsanity.com/paper/1903.00967/) (2019)
- [Proportionality and the Limits of Welfarism](https://mlsanity.com/paper/1911.11747/) (2019)
- [Clustering attributed graphs: models, measures and methods](https://mlsanity.com/paper/1501.01676/) (2015)
- [Fairness-Aware Node Representation Learning](https://mlsanity.com/paper/2106.05391/) (2021)
- [Participatory Budgeting: Data, Tools, and Analysis](https://mlsanity.com/paper/2305.11035/) (2023)
- [What Do Multiwinner Voting Rules Do? An Experiment Over the Two-Dimensional Euclidean Domain](https://mlsanity.com/paper/1901.09217/) (2019)
- [FairSNA: Algorithmic Fairness in Social Network Analysis](https://mlsanity.com/paper/2209.01678/) (2022)
- [Proportionally Representative Clustering](https://mlsanity.com/paper/2304.13917/) (2023)
- [Approximate Group Fairness for Clustering](https://mlsanity.com/paper/2203.17146/) (2022)
- [Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?](https://mlsanity.com/paper/2206.03262/) (2022)
- [Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias](https://mlsanity.com/paper/2309.14907/) (2023)
- [Strategyproof Mechanisms For Group-Fair Facility Location Problems](https://mlsanity.com/paper/2107.05175/) (2021)
- [Absorbing random-walk centrality: Theory and algorithms](https://mlsanity.com/paper/1509.02533/) (2015)
- [Method of Equal Shares with Bounded Overspending](https://mlsanity.com/paper/2409.15005/) (2024)
- [Fairness in Maximal Covering Location Problems](https://mlsanity.com/paper/2204.06446/) (2022)
- [Proportional Fairness in Obnoxious Facility Location](https://mlsanity.com/paper/2301.04340/) (2023)
- [The Squared Kemeny Rule for Averaging Rankings](https://mlsanity.com/paper/2404.08474/) (2024)
- [Centrality with Diversity](https://mlsanity.com/paper/2101.07371/) (2021)
- [Group Centrality Maximization for Large-scale Graphs](https://mlsanity.com/paper/1910.13874/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fairness in the Multi-Secretary Problem](https://mlsanity.com/paper/2511.23097/) (2025)

## Abstract (arXiv)

We address the problem of selecting k representative nodes from a network, aiming to achieve two objectives: identifying the most influential nodes and ensuring the selection proportionally reflects the network's diversity. We propose two approaches to accomplish this, analyze them theoretically, and demonstrate their effectiveness through a series of experiments.