OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

Dualfunctional MEMS optical device with compound electrostatic actuators for compact and flexible photonic networks

A MEMS (micro-electromechanical systems) reflection-type dualfunction-integrated optical device is proposed. The device employs the compound in-plane and out-of-plane motion of a dual-slope mirror, which is driven by electrostatic actuators, to operate as optical switch and variable optical attenuator, independently. The MEMS-based dualfunctional devices can minimize the overall system size and weight, reduce external interconnections between individual devices, while at the same time maximize system information capacity, optical throughput, flexibility and reliability. Measurements of the MEMS-based dualfunctional devices show that the switching time is less than 9 ms, the excess loss is less than 3 dB and the controllable attenuation range is up to 39 dB, respectively. Moreover, polarization-dependent loss is less than 0.7 dB in the whole attenuation range.Engineering, Electrical & ElectronicInstruments & InstrumentationEICPCI-S(ISTP)