graph neural networks

VulGNN: A high-fidelity graph neural network framework for robust smart contract vulnerability detection

EdgeSAC: Graph Neural Soft Actor-Critic for Hierarchical IoV Resource Management

Intelligent Transportation Systems (ITS) rely on the Internet of Vehicles (IoV) to sustain high data rates and low latency under dynamic and heterogeneous conditions. Joint power and spectrum control across macro and micro tiers remains challenging due to mobility, interference coupling, and large continuous action spaces. EdgeSAC is a graph-aware Soft Actor Critic (SAC) framework executed at the edge for power control in hierarchical Fifth-Generation New Radio (5G NR) Multiple-Input Multiple-Output (MIMO) networks. A permutation-equivariant Graph Neural Network (GNN) with edge updates encodes co-channel interference among Base Stations (BSs) and outputs node-level power fractions under tier budgets. An on-demand scheduler activates fixed-size channels and assigns at most one macro and one micro resource per user to realize dual connectivity. Signal-to-Interference-plus-Noise Ratio (SINR) is mapped to rate using a Shannon with gap model with rank adaptive MIMO, enabling tier aggregation without action discretization. In simulation with Third Generation Partnership Project (3GPP) TR 38.901 path loss and Manhattan mobility, EdgeSAC increases throughput over SAC and Proximal Policy Optimization (PPO) and reduces power relative to Twin Delayed Deep Deterministic Policy Gradient (TD3), which raises energy efficiency and fairness. The findings indicate that interference-aware graph embeddings combined with entropy regularized continuous control provide a scalable and power-efficient solution for hierarchical IoV resource management.

Sustainable energy management in IoT-enabled hybrid microgrids via graph neural network-optimized automatic generation control

Automated generation and surrogate modeling of 3D urban wind simulation based on physics-informed graph neural networks: a two-stage framework

Construction supply-chain carbon footprint with graph neural network-based input-output framework

Information bottleneck-based correlation analysis with graph neural ODEs for network reconstruction

Modeling adolescents' perception of cycling safety: A new approach using graph neural networks and street view imagery

Graph neural network-based multi-agent reinforcement learning for active voltage control: Performance and topology robustness

A deep reinforcement learning method based on scale perception and heterogeneous graph neural network for flexible job shop scheduling

Syntactic enhancement and redundant feature elimination in text graph neural networks for propaganda detection

Cross-domain integration of transcriptomic modalities through graph neural networks

Exploring graph neural networks for flood modeling: Challenges, opportunities, and future prospects