graph neural networks

##基于自适应的图神经网络的复杂性分析

This paper presents a novel framework for analyzing the complexity of graph neural networks (GNNs) utilizing adaptive graph neural networks (GNNs). The core aim is to automatically identify and quantify the complexity of a GNN, providing actionable complexity control strategies. We propose a dynamic complexity adjustme...

Non-Linear Geometric Constraints: A Graph-Based Optimization Algorithm

This paper introduces a novel graph-based optimization algorithm specifically designed to address non-linear geometric constraints. Traditional optimization methods often struggle with complex geometric designs due to the inherent limitations of their geometric representation. This work proposes a framework leveraging...

Dynamic Graph Neural Networks for Autonomous Robot Navigation

Autonomous robot navigation necessitates robust perception and decision-making capabilities that can adapt to dynamic and unpredictable environments. Traditional graph neural networks (GNNs) often rely on static graph structures, failing to effectively represent and reason about the constantly evolving relationships wi...

基于自适应的深度学习的动态图神经网络

This paper introduces a novel dynamic graph neural network (GNN) architecture leveraging adaptive deep learning techniques to dynamically adjust graph connectivity based on input data. Traditional GNN models often require manual parameter tuning, limiting their adaptability to dynamic datasets. Our proposed approach em...

forest-microclimate-loso-gnn: analysis scripts and public-safe aggregate outputs for 'Dual-variable evaluation of an inductive graph neural network for hourly forest microclimate reconstruction at spatially held-out sites'

##基于图神经网络的药物分子设计

This paper explores the application of Graph Neural Networks (GNNs) for automated drug molecule design. Traditional drug discovery relies heavily on empirical methods and serendipitous findings, often suffering from high costs and low success rates. We propose a novel approach that leverages the inherent structural inf...

Explainable AI for Graph Structure Discovery

Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems, by effectively learning patterns from graph-structured data. However, a significant limitation of GNNs is their inherent opaqueness—the "black box" nature...

##基于图神经网络的复杂网络拓扑识别

This paper investigates the application of Graph Neural Networks (GNNs) for the automated identification and analysis of complex network topologies. Traditional network analysis methods often struggle to capture the intricate relationships and emergent patterns found in complex networks, such as social networks, biolog...

Causal Discovery from Observational Data using Graph Neural Networks

This paper explores the application of Graph Neural Networks (GNNs) for causal discovery directly from observational data. Traditional causal inference methods often rely on strong assumptions regarding underlying causal mechanisms, which can be difficult to validate. We propose a novel framework where a GNN learns cau...

Cognitive Graph Representation of Human Memory

This paper proposes a novel approach to modeling human memory by representing it as a dynamic cognitive graph. The core idea is to map the complex neural activation patterns observed during memory processes onto a graph structure where nodes represent concepts and edges represent the strength and temporal relationships...

Temporal heterogeneous graph neural networks for multimodal rumor detection on social media

THGNN-MRD, a temporal heterogeneous graph neural network for multimodal rumor detection, is proposed, suggesting that modeling rumors as evolving multimodal social events provides a principled and effective solution for trustworthy rumor detection.

An intelligent cyber-attack detection framework for healthcare cyber-physical systems using optimized graph-based deep learning

The proposed CSGSACNN-HMOA framework operates to safeguard data integrity and patient safety at the same time as facilitating secure deployment in scalable healthcare environments and reaches superior results in every essential measurement aspect.