graph neural networks

Temporal Graph Neural Networks with Adaptive Relational Strength

This paper introduces a novel approach to Graph Neural Networks (GNNs) designed to effectively capture temporal dependencies within dynamic graph structures. Traditional GNNs often fail to adequately represent the evolving nature of relationships between nodes, hindering their performance in systems where the graph top...

Modularity-Based Linkage Model Assisted Gradient Descent for Neural Network Training

基于图的动态拓扑结构优化算法

This paper introduces a novel dynamic topology optimization algorithm based on graph neural networks (GNNs). Traditional topology optimization methods are often static and require manual parameter tuning. Our algorithm automatically adjusts the topology structure based on the system's inherent properties, offering sign...

基于自适应图神经网络的动态量子算法

This paper presents a novel dynamic quantum algorithm leveraging self-adaptive graph neural networks (S-GNNs). Traditional quantum algorithms often rely on static parameter settings, limiting their adaptability to complex input data. Our approach introduces a dynamically adjusting S-GNN that automatically adjusts the c...

基于图的自适应量子拓扑优化

The development of quantum information processing has spurred significant research into quantum topology, offering the potential for novel quantum algorithms and devices. However, designing and optimizing quantum circuits with complex topologies remains a challenging task. This paper proposes a novel algorithm, "基于图...

CAUSTIC: conformation-aware uncertainty and shift prediction from protein conformer ensembles

CAUSTIC is a PaiNN equivariant graph neural network that predicts protein backbone NMR chemical shifts (H, HA, N, CA, CB, C') with calibrated uncertainties from PDB, mmCIF or AlphaFold structures. This record archives the source code of the caustic-nmr Python package together with the bundled ONNX model weights and pos...

基于图神经网络的程序代码语义理解与重构

This paper proposes a novel approach to program code semantic understanding and reconstruction leveraging Graph Neural Networks (GNNs). The core idea is to directly learn semantic relationships within program code by representing it as a graph structure. Nodes in the graph represent code elements such as functions, var...

基于图的自适应图神经网络

This paper introduces a novel graph neural network (GNN) architecture, termed "Adaptive Graph Neural Network" (AGNN), designed to dynamically adjust its graph structure in response to data fluctuations. The AGNN's core mechanism centers around a dynamic parameterization of the graph, enabling efficient knowledge transf...

基于图的机器学习的动态路径规划

This paper investigates the application of Graph Neural Networks (GNNs) to dynamic path planning, offering a novel approach to address the limitations of traditional methods. Dynamic path planning necessitates the ability to adapt to changing environmental conditions and real-time data, which is often challenging with...

GDAN-ID: Subject-independent EEG emotion recognition via graph diffusion attention network with INRDecoder

基于图的复杂性学习的自适应几何建模

This paper introduces a novel approach to geometric modeling that leverages graph neural networks (GNNs) for adaptive complexity learning. Traditional methods often require manual design of complex geometric models, leading to high modeling costs. We propose a system that dynamically adjusts the complexity of GNNs base...

基于图神经网络的系统状态预测与协同控制

This paper introduces a novel system of framework for predicting system states and enabling collaborative control utilizing graph neural networks (GNNs). The core idea is to leverage graph structure to model system dynamics, identify key nodes and their relationships, and facilitate efficient control strategies. We pro...