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Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
Yufeng Wang, Yiguang Bai, Tianqing Zhu, Ismail Ben Ayed, Jing Yuan
https://arxiv.org/abs/2505.22684

Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning
Community partitioning is crucial in network analysis, with modularity optimization being the prevailing technique. However, traditional modularity-based methods often overlook fairness, a critical aspect in real-world applications. To address this, we introduce protected group networks and propose a novel fairness-modularity metric. This metric extends traditional modularity by explicitly incorporating fairness, and we prove that minimizing it yields naturally fair partitions for protected gro…

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
Zhengyang Shan, Emily Ruth Diana, Jiawei Zhou
https://arxiv.org/abs/2506.15568

Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models
We present a comprehensive evaluation of gender fairness in large language models (LLMs), focusing on their ability to handle both binary and non-binary genders. While previous studies primarily focus on binary gender distinctions, we introduce the Gender Inclusivity Fairness Index (GIFI), a novel and comprehensive metric that quantifies the diverse gender inclusivity of LLMs. GIFI consists of a wide range of evaluations at different levels, from simply probing the model with respect to provide…

Decoupling Structural Heterogeneity from Functional Fairness in Complex Networks: A Theoretical Framework based on the Imbalance Metric
Zhiyuan Ren, Zhiliang Shuai, Wenchi Cheng, Kun Yang
https://arxiv.org/abs/2508.06898

Decoupling Structural Heterogeneity from Functional Fairness in Complex Networks: A Theoretical Framework based on the Imbalance Metric
Performance evaluation of complex networks has traditionally focused on structural integrity or average transmission efficiency, perspectives that often overlook the dimension of functional fairness. This raises a central question: Under certain conditions, structurally heterogeneous networks can exhibit high functional fairness. To systematically address this issue, we introduce a new metric, Network Imbalance (I), designed to quantitatively assess end-to-end accessibility fairness from a perc…

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
Ali Khandan Boroujeni, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Stefan K\"opsell, Ghazal Bagheri, Rafael F. Schaefer
https://arxiv.org/abs/2507.11224

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy rate under constraints balancing both communication and sensing requirements. To enhance fairness among users, an entropy-regularized fairness metric is introduced within the problem framework. We then propose a solution employing an accelerated quadratic transform (QT) with a …

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
Pouya Agheli, Tugce Kobal, Fran\c{c}ois Durand, Matthew Andrews
https://arxiv.org/abs/2506.08263

Learning-Based Multiuser Scheduling in MIMO-OFDM Systems with Hybrid Beamforming
We investigate the multiuser scheduling problem in multiple-input multiple-output (MIMO) systems using orthogonal frequency division multiplexing (OFDM) and hybrid beamforming in which a base station (BS) communicates with multiple users over millimeter wave (mmWave) channels in the downlink. Improved scheduling is critical for enhancing spectral efficiency and the long-term performance of the system from the perspective of proportional fairness (PF) metric in hybrid beamforming systems due to …