Graph Neural Networks Based Analog Circuit Link Prediction

ReassembleNet is a novel approach to the challenge of reassembly in various fields such as archaeology and genomics. It addresses key limitations in existing Deep Learning methods, including scalability and real-world applicability. By representing input pieces as contour keypoints and utilizing Graph Neural Networks, ReassembleNet reduces computational complexity while integrating features from multiple modalities, enhancing its effectiveness in reconstructing complex structures.

Certified Signed Graph Unlearning (CSGU) addresses the challenges of privacy in Signed Graph Neural Networks (SGNNs). Traditional graph unlearning methods fail to preserve the unique properties of signed graphs, leading to loss of critical sign information. CSGU introduces a three-stage approach to effectively remove specific data influences while maintaining privacy guarantees and the sociological principles of SGNNs. This advancement is crucial for applications where data sensitivity is paramount.

The paper introduces DualLaguerreNet, a new architecture for Graph Neural Networks (GNNs) that addresses the limitations of single-filter models like LaguerreNet. By decoupling the graph Laplacian into low and high-frequency operators, it allows for independent learning of two adaptive Laguerre polynomial filters. This innovation aims to overcome the compromise problem of averaged responses across the graph spectrum, enhancing performance on complex heterophilic graphs.

The accurate prediction of flow fields around airfoils is essential for aerodynamic design and optimization. While Computational Fluid Dynamics (CFD) models are effective, they are computationally expensive. This has led to the development of surrogate models using deep learning architectures, including Convolutional Neural Networks (CNNs) and Diffusion Models (DMs). The proposed model, FoilDiff, utilizes a hybrid-backbone denoising network that combines convolutional feature extraction with transformer-based global attention, enhancing adaptability and accuracy in flow structure representatio…

The paper titled 'Fairness-Aware Graph Representation Learning with Limited Demographic Information' addresses the challenge of ensuring fairness in Graph Neural Networks (GNNs). It highlights that many existing methods require complete demographic data, which is often unavailable due to privacy concerns. The authors propose a new framework that generates proxies for demographic information using partial data and enforces consistent node embeddings across demographic groups, along with an adaptive confidence strategy to balance fairness and utility.

Graph Neural Networks (GNNs) have shown significant success in various applications, but they face challenges such as oversmoothing and inefficiency on heterophilic graphs. To overcome these limitations, a new framework is introduced that incorporates complex weights into graph structures, allowing for a complex diffusion process. This leads to the development of the Complex-Weighted Convolutional Network (CWCN), which learns complex-weighted structures from data and enhances diffusion with learnable matrices and nonlinear activations, proving to be effective for node-classification tasks.