graph neural networks

Network-on-Chip Congestion Prediction Using Spatiotemporal Graph Neural Networks and a Neo4j Graph Database

Reproducibility artifact for an offline framework that predicts short-term per-router Network-on-Chip congestion, integrating a customised BookSim2 simulator, a Neo4j unified graph store for static topology and dynamic state, and a multi-seed comparison of topology-aware and topology-agnostic next-step buffer-occupancy...

Temporal Graph Neural Networks for Predictive System Dynamics

This paper investigates the application of Temporal Graph Neural Networks (T-GNNs) for predicting the dynamics of complex systems. Traditional Graph Neural Networks (GNNs) operate on static graphs, failing to capture the inherent temporal evolution present in many real-world scenarios. We propose a novel framework util...

Truth inference for crowdsourcing via bias-variance fusion graph embedding

This article introduces two metrics—bias and variance—to quantify, respectively, the discrepancy between a worker’s labels and those of other workers on similar instances (bias), and the inconsistency in a worker’s own labels across different instances (variance).

Congestion Propagation Identification and Prediction Using Self Attention–Based Diffusion Convolutional Approach

A propagation probability matrix is utilizes to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network and proposing a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffi...

Dynamic Semantic Topology Learning and Program Reasoning

This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its run...

Based on Multi-modal Data Fusion for Subconscious Decision Simulation

This paper presents a novel approach to simulating subconscious decision-making processes by leveraging multi-modal data fusion. The core idea is to construct a computational model capable of mirroring the complexities of human subconscious decision-making, moving beyond traditional behavioral analysis. We employ a gra...

Learning Topological Features of $\widehat Z$-invariants

This paper initiates a systematic approach to handling mathematical data structured as (truncated) infinite $q-series, or equivalently, infinite series of integers, and demonstrates that neural networks can reliably extract essential topological information, such as homology class and underlying graph structure, direct...

Physics-structured graph neural networks for interpretable fault detection in semiconductor manufacturing processes

Integrating ENSO dynamics into physics-informed Graph Neural Networks for extreme evapotranspiration prediction under climate change

A physics-informed wavelet-based multi-scale graph neural network for aero-engine remaining useful life prediction

Parallel Dual-Level Attention-Enhanced Semantic Segmentation of 3D Point Clouds in Tunnel Construction

Validated on real tunnel data, PDAPointNet++ outperforms models such as PointNet, PointNet++, PointMLP, and dynamic graph convolutional neural network (DGCNN) and the impact of different WDFL balancing coefficients on model performance is discussed.

Graph neural network enhanced large language model for commonsense question answering