graph neural networks

Imitation Learning for Connection-Tableau Construction

An automated theorem prover builds a proof step by step, choosing at each point what to add and what to remove, and learns policies that solve up to 46% more problems than leanCoP, and reach proofs in an order of magnitude fewer steps.

Feasibility Study on Inferring Construction Workers’ Roles and Cooperation Status Using Graph Neural Networks

CrystalGRW: generative modeling of crystal structures with targeted crystallographic properties via geodesic random walks

CrystalGRW is introduced, a diffusion-based generative model on Riemannian manifolds that proposes candidate crystal configurations in stable phases, validated through density functional theory calculations, thereby accelerating materials discovery and inverse design.

基于多模态信息融合的神经符号推理引擎

This paper introduces a novel neuro-symbolic reasoning engine designed to tackle complex problems by integrating the strengths of deep learning and symbolic reasoning. The core concept is to fuse multi-modal information – including images, text, and audio – to create a richer understanding of the problem domain. This u...

Enhanced air quality prediction using probabilistic spiking neural networks with multi-aspect graph attention mechanism

基于自适应的神经网络的图论优化

This paper presents a novel graph theory-based optimization algorithm for neural network architecture design, leveraging the inherent properties of graph structures to enhance network performance. Traditional neural network architectures often rely on fixed configurations, while this approach dynamically adjusts networ...

##基于可扩展图神经网络的软件漏洞自动检测

This paper presents a novel approach to automated software vulnerability detection using scalable graph neural networks (GNNs). Traditional vulnerability detection methods often rely on manual code review or signature-based approaches, which are labor-intensive, error-prone, and struggle to identify novel vulnerabiliti...

Graph-Based Representation Learning for Sensor Networks with Dynamic Topology

This paper introduces a novel graph embedding approach specifically designed for sensor networks exhibiting dynamic topologies. Traditional graph embedding techniques often rely on static graph structures, rendering them ineffective in environments where network connections are constantly changing due to node failures,...

Title: Dynamic Probabilistic Graphs via High-Dimensional Neural Networks: Semantic Reasoning and Knowledge Graph Construction in Natural Language Processing

This paper introduces a novel dynamic probabilistic graph model, leveraging high-dimensional neural networks, for natural language processing. We propose a mechanism that dynamically adjusts the network parameters to enhance both semantic reasoning and knowledge graph construction capabilities. The core challenge lies...

基于图神经网络的程序合成与优化

This paper explores the application of Graph Neural Networks (GNNs) to the fields of program synthesis and optimization. The core idea is to represent program code as graph structures and leverage the power of GNNs to automatically learn the underlying structure and semantics. This approach offers a novel pathway for p...

Decentralized Learning with Proof-of-Work Consensus for Graph Neural Networks

This paper introduces a novel decentralized learning framework for Graph Neural Networks (GNNs) leveraging a Proof-of-Work (PoW) consensus mechanism. Traditional GNN training relies heavily on centralized servers, leading to vulnerabilities regarding single points of failure and hindering scalability. Our approach addr...

Graph Neural Network-Based Program Optimizer

This paper introduces a novel approach to program optimization leveraging the power of Graph Neural Networks (GNNs). The core idea is to represent program abstractions, such as Control Flow Graphs (CFGs) and Data Dependency Graphs (DDGs), as graphs and utilize GNNs to automatically identify optimization opportunities a...