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 enhances the ability to perform sophisticated reasoning tasks compared to traditional, single-modality approaches. The proposed mechanism centers around constructing a fused knowledge graph encompassing textual, visual, and auditory data, coupled with a graph neural network (GNN)-based reasoning engine. This architecture overcomes the limitations inherent in single-modality knowledge graphs, offering a more comprehensive and nuanced understanding of interconnected information. The engine is designed to dynamically adapt to the relationships between different modalities, improving the accuracy and efficiency of reasoning processes. The paper outlines the architecture, the GNN implementation, and potential applications, illustrating a pathway toward intelligent systems capable of sophisticated knowledge extraction and inference.

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 capabilities of symbolic reasoning systems. This hybrid architecture aims to construct AI systems that not only possess high accuracy but also offer enhanced interpretability and reliability. The system utilizes a neural network to process raw input data and generate a structured knowledge graph, which is then subjected to symbolic reasoning algorithms to derive conclusions and make decisions. The proposed method addresses the limitations of existing neural AI approaches that often lack explicit knowledge representation and reasoning mechanisms, leading to a lack of transparency and difficulty in debugging. This research contributes to the development of more robust and trustworthy AI systems by grounding AI decisions in a formally represented knowledge base. The evaluation framework will focus on assessing the system's accuracy, interpretability, and robustness across various reasoning tasks.

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, responding to evolving input data and task requirements. The core mechanism involves a graph-based neural network framework where neurons and connections form a dynamic topology. Each neuron possesses a state vector, influencing its activation level, connection weights, and local environmental information. Topological evolution is governed by mechanisms, such as genetic algorithms or reinforcement learning, dynamically adjusting the network's structure by adding, removing, or modifying connections based on neuron states and network performance metrics. Simultaneously, feedback learning updates neuron activation levels through gradient descent or pulse feedback, driven by input signals and performance objectives. This architecture offers enhanced adaptability, robustness, and scalability, surpassing the limitations of static networks in handling unstructured and dynamic data. The key innovation lies in the dynamic control of topology and the deep learning of neuron states, providing a more flexible and powerful framework for complex data processing.

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 topology changes over time. Our proposed Temporal Graph Neural Networks (T-GNNs) address this limitation by incorporating a 'temporal strength' factor into node embeddings. This factor is dynamically adjusted based on the observed evolution of relationships within the graph, learned through a recurrent attention mechanism that specifically focuses on relational changes. The core innovation lies in the adaptive adjustment of relational strength, allowing the network to prioritize relevant temporal information. We demonstrate through a theoretical analysis and structural design that this approach significantly improves the ability of GNNs to model time-evolving graph data. The resulting T-GNN architecture offers a robust framework for analyzing and predicting behavior in dynamic systems represented as graphs.

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 *meaning* of the graph, achieved through a mechanism that learns representations sensitive to the graph's structure and content. This approach aims to improve generalization and robustness compared to existing methods, particularly in scenarios with complex, high-dimensional graphs. The core of this work involves a modified attention mechanism integrated within the graph neural network to facilitate the propagation of information across the graph, thereby strengthening the representation of semantic connections. We present a series of experiments evaluating the performance of this architecture on various graph datasets, demonstrating significant improvements in semantic understanding and overall accuracy.

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 topology changes over time. Our proposed Temporal Graph Neural Networks (T-GNNs) address this limitation by incorporating a 'temporal strength' factor into node embeddings. This factor is dynamically adjusted based on the observed evolution of relationships within the graph, learned through a recurrent attention mechanism that specifically focuses on relational changes. The core innovation lies in the adaptive adjustment of relational strength, allowing the network to prioritize relevant temporal information. We demonstrate through a theoretical analysis and structural design that this approach significantly improves the ability of GNNs to model time-evolving graph data. The resulting T-GNN architecture offers a robust framework for analyzing and predicting behavior in dynamic systems represented as graphs.

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 significant improvements in efficiency and robustness. We propose a framework that leverages graph representations to capture the system's topology, enabling a more adaptable and efficient optimization process. This work addresses a critical limitation of existing methods by providing a dynamic and intelligent approach to topology design. The core mechanism centers around utilizing GNNs to represent and manipulate the topology, resulting in optimized solutions with enhanced adaptability. We present a comprehensive analysis of the algorithm's performance, including experimental results demonstrating its effectiveness across various topology configurations.

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 connection weights and neuron counts, enabling more efficient quantum computation. We demonstrate the algorithm's effectiveness through a series of benchmark tests, highlighting its ability to navigate intricate quantum circuits and achieve improved performance compared to existing methods. The core mechanism centers on a feedback loop that continuously refines the network based on the input, optimizing for a specific quantum circuit's properties. This contributes to a significant advancement in quantum algorithm design and optimization.

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, "基于图的自适应量子拓扑优化", that leverages graph neural networks (GNNs) to dynamically adjust quantum topology during optimization. We demonstrate the effectiveness of this approach through the optimization of a specific quantum circuit, showcasing improved convergence and efficiency compared to traditional methods. This work introduces a framework that intelligently refines quantum states based on the underlying topology, addressing a key limitation of existing approaches.

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 post-prediction calibrator. Code is MIT-licensed; the model weights and calibrator are CC BY 4.0 (see LICENSE-WEIGHTS). Method, data, benchmark protocol and limitations are documented in the repository under docs/.