graph neural networks

Multi-Modal Knowledge Graph Reasoning Engine

This paper introduces a novel Multi-Modal Knowledge Graph Reasoning Engine designed to facilitate complex reasoning and knowledge discovery through the interactive integration of diverse knowledge graph modalities. The core claim is that leveraging the interplay between multi-modal knowledge graphs significantly enhanc...

基于神经符号人工智能的知识推理

This paper explores a novel approach to artificial intelligence by integrating the strengths of neural networks and symbolic reasoning. The core idea is to leverage the pattern recognition capabilities of neural networks for knowledge extraction and representation, coupled with the logical deduction and inference capab...

Dynamic Topology Neural Networks (DTNNs) Learning

This paper introduces Dynamic Topology Neural Networks (DTNNs), a novel approach to neural network design that leverages continuous topological evolution and feedback learning to autonomously construct and optimize network topologies. Unlike traditional static neural networks, DTNNs adapt their structure in real-time,...

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...

RGDLAN and HMARN: A dual-path graph neural approach with multi-agent systems for listed enterprise violation prediction

Title: Non-Local Graph Neural Networks for Semantic Representation

This paper explores the application of non-local graph neural networks to enhance semantic representation learning. Traditional graph neural networks primarily focus on node features, often struggling to capture the nuanced relationships within a graph. We propose a novel architecture that explicitly models the *meanin...

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...