graph neural networks

Dependency Discovery Based on Topological Information

This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A graph neural network (GNN) algorithm is developed to learn program representations and infer dependencies using graph convolutional operations. Furthermore, execution data is utilized to validate and refine the dependency graph. This method overcomes limitations of existing static analysis and monitoring-based tools by effectively utilizing program abstract representations, enabling the discovery of intricate dependencies often missed by traditional approaches. The research contributes to a more complete understanding of software ecosystems and facilitates improved dependency management, version control, and software evolution.

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 predictors.

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 utilizing GNNs that explicitly incorporate time-dependent graph structures, leading to enhanced predictive accuracy. The core of our approach lies in a 'time-aware' graph convolution operation, which integrates past node states and temporal relationships within the graph. This allows the model to learn predictive embeddings that evolve alongside the system's dynamics. Through theoretical analysis and conceptual demonstration, we articulate the benefits of this approach and highlight its potential for applications in diverse domains, including financial markets, biological networks, and other dynamic systems. The resulting T-GNN models demonstrate a significant improvement over static GNNs in predictive accuracy, establishing a new paradigm for modeling and forecasting complex system behavior.

Truth inference for crowdsourcing via bias-variance fusion graph embedding

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

Abstract Traffic congestion frequently propagates among the interconnected road networks over time, driven by spatial and temporal factors. Identifying and predicting these patterns is crucial for effective public transport management and urban planning. Existing systems often fail to accurately capture the complex diffusion properties of traffic flow and spatial dependencies, resulting in less reliable predictions. This study utilizes a propagation probability matrix to identify congestion propagation patterns and finds traffic behavior over 24 h, revealing critical insights into congestion trends in a selected road network. In addition, to overcome the remaining limitations, we propose a novel self attention–based diffusion convolutional network (SADCN) that effectively predicts traffic congestion propagation. The proposed model incorporates key spatial relations, including adjacency, diffusion, and propagation probability matrices, to improve the understanding of congestion dynamics. To demonstrate the significance of SADCN, we compare its performance with several recent graph-based models, including fully connected long short-term memory (FC-LSTM), diffusion convolutional recurrent neural network (DCRNN), and attention-based spatial-temporal graph convolutional network (ASTGCN). Compared with existing models, the proposed method achieved superior results, with an accuracy of 0.976, a precision of 0.950, a recall of 0.942, and F 1 -score of 0.946 for 50 epochs. Furthermore, the model outperformed state-of-the-art methods at shorter intervals, such as 5, 10, and 20 epochs, highlighting its faster convergence and efficiency in predicting traffic congestion propagation patterns to improve public transport systems.

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 runtime environment, which evolves during execution. We propose a mechanism utilizing Graph Neural Networks (GNNs) to construct and continuously update a program semantic topology. Nodes in this topology represent program elements such as functions, variables, and data structures, while edges represent their semantic dependencies – including call relationships, data flow relationships, and control flow relationships. A reinforcement learning-based GNN learning algorithm leverages feedback signals from program execution (e.g., results, errors, resource consumption) to adjust the topology, enhancing its accuracy in reflecting program behavior. This dynamic approach overcomes limitations of static semantic representations like Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) by capturing the complex, runtime-dependent relationships, ultimately leading to improved program understanding and optimization. The methodology offers a new paradigm for analyzing and reasoning about program logic, particularly in dynamic and complex systems.

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 graph neural network (GNN) architecture for robust multi-modal data integration, transforming diverse data streams – including visual, auditory, and tactile information – into a unified representation. This representation is then utilized within a reinforcement learning framework to simulate the subconscious decision-making process, explicitly modeling the interactive effects between different modalities. The resulting model provides a deeper understanding of how individuals make decisions without conscious awareness, offering potential applications in fields such as robotics, human-computer interaction, and cognitive modeling. The key innovation lies in the comprehensive incorporation of multi-modal interactions, providing a more accurate representation of the human subconscious than existing approaches. We define the following key equations to represent the core processes within the model: Let *xi* represent the input vector for modality *i*, where *i* ∈ {V, A, T}, representing Visual, Auditory, and Tactile modalities, respectively. The dimensionality of each *xi* is denoted as *di*. The multi-modal fusion process can be expressed as: * *xfused* = FusionNetwork(*xV*, *xA*, *xT*) Where *xfused* is the fused representation and FusionNetwork is the graph neural network. The reinforcement learning agent's decision-making process is governed by the following equation: * *ai* = argmaxj [Q( *xfused*, *aj* ) + β * R( *xfused*, *aj*)] Where *ai* is the action taken, *Q* is the Q-function estimating the expected reward, *R* is the reward function, and β is a weighting factor. The model's training objective can be formalized as: Minimize Eτ [ Σt=0T γt *R( *xfused*, *at* )] Where τ is a trajectory, *R* is the reward function, γ is the discount factor, and T is the time horizon.

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