graph neural networks

Research on the Fraud Detection Model of Related Party Transactions by Integrating Graph Neural Networks and XGBoost

Facial expression recognition using a non-exclusive learning search-Fossa optimization algorithm with a convolutional neural network

A non-exclusive learning search-Fossa optimization algorithm integrated with a convolutional neural network (NELS-FOA-CNN) is proposed to select the most relevant features for accurate FER, demonstrating improved performance compared with the baseline GCN.

基于自适应的动态图神经网络

This paper introduces a novel dynamic graph neural network (D-GNN) architecture, termed "Adaptive Dynamic Graph Neural Network," designed to optimize the learning process of GNNs through dynamic adjustment of the graph's structure and parameters. Traditional GNN approaches often rely on fixed graph structures, limiting...

Dynamic Semantic Graph Construction and Reasoning

This paper proposes a novel framework for constructing and reasoning with dynamic semantic graph knowledge. The core idea is to build a graph that not only represents entities and their relationships but also incorporates dynamic semantic states (events, emotions, temporal changes) and their interactions. This dynamic...

##基于同理心计算的社会网络建模

This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research intro...

基于多模态关联的概率推理网络

This paper proposes a novel probabilistic inference network designed to leverage the correlations inherent within multi-modal data. The core idea is to construct a network architecture that explicitly models and utilizes relationships between different data modalities. This is achieved through the application of Graph...

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...

Title: Neuro-Symbolic Reasoning via Dynamic Graph Construction (NSRC-DG)

Neuro-Symbolic Reasoning via Dynamic Graph Construction (NSRC-DG) presents a novel approach to reasoning that integrates the strengths of neural networks and symbolic reasoning. Traditional reasoning systems often struggle with complex, nuanced inferences requiring deep understanding and logical deduction. This work in...

**Temporal Graph Neural Networks with Dynamic Path Encoding**

This record contains a research paper preprint and its associated PDF document.

Temporal Topology Modeling for Signal Processing

Temporal Topology Modeling for Signal Processing addresses the limitations of traditional signal processing methods by introducing a novel temporal topology model. This model leverages the combined power of recurrent neural networks and graph theory to effectively capture the evolution of signal patterns over time. The...

Temporal Graph Embedding with Relational Causality

This paper introduces a novel approach to graph embedding that explicitly incorporates temporal dynamics and relational causality. Existing graph embedding methods often treat graphs as static structures, neglecting the evolving nature of relationships and the underlying causal mechanisms that govern them. We propose a...

基于图的自然语言语法推断的自适应规则引擎

This paper introduces a novel self-adaptive grammar rule engine based on graph neural networks. The engine leverages a graph representation of the input text to automatically infer grammatical rules and generate more accurate translations. We propose a method that dynamically adjusts the graph structure based on contex...