Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies on poorly constructed semi-synthetic datasets or substandard real-world datasets. In such cases, even a basic Multilayer Perceptron (MLP) can outperform Graph Neural Networks (GNNs) in both utility and fairness. In this work, we illustrate that many datasets fail to provide meaningful information in the edges, which may challenge the necessity of using graph structures in these problems. To address these issues, we develop and introduce a collection of synthetic, semi-synthetic, and real-world datasets that fulfill a broad spectrum of requirements. These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models. The proposed synthetic and semi-synthetic datasets offer the flexibility to create data with controllable bias parameters, thereby enabling the generation of desired datasets with user-defined bias values with ease. Moreover, we conduct systematic evaluations of these proposed datasets and establish a unified evaluation approach for fair graph learning models. Our extensive experimental results with fair graph learning methods across our datasets demonstrate their effectiveness in benchmarking the performance of these methods. Our datasets and the code for reproducing our experiments are available at https://github.com/XweiQ/Benchmark-GraphFairness.

Upper Bounding Barlow Twins: A Novel Filter for Multi-Relational Clustering

Multi-relational clustering is a challenging task due to the fact that diverse semantic information conveyed in multi-layer graphs is difficult to extract and fuse. Recent methods integrate topology structure and node attribute information through graph filtering. However, they often use a low-pass filter without fully considering the correlation among multiple graphs. To overcome this drawback, we propose to learn a graph filter motivated by the theoretical analysis of Barlow Twins. We find that input with a negative semi-definite inner product provides a lower bound for Barlow Twins loss, which prevents it from reaching a better solution. We thus learn a filter that yields an upper bound for Barlow Twins. Afterward, we design a simple clustering architecture and demonstrate its state-of-the-art performance on four benchmark datasets.

Sensing-based Beamforming Design for Joint Performance Enhancement of RIS-Aided ISAC Systems

Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC.