graph neural networks

Self-Supervised Learning for Discovering Graph Embeddings

Graph neural networks (GNNs) have achieved significant success in various graph-related tasks, including node classification, link prediction, and graph classification. However, a critical limitation of many GNN approaches is their dependence on large amounts of labeled data for training. Obtaining such labeled data ca...

Graph Neural Networks for Personalized Cybersecurity Threat Detection

This paper proposes a novel approach to cybersecurity threat detection leveraging Graph Neural Networks (GNNs) for personalized modeling. Traditional cybersecurity solutions often rely on generic, rule-based systems that struggle to adapt to the diverse and evolving nature of network environments. We introduce a framew...

Title: Temporal Topology – Predictive Geometry from Event Streams

This paper introduces Temporal Topology, a novel system for predicting future topology based on event streams. We propose a Temporal Graph Network, a neural network trained to learn geometric patterns from sequences of events, allowing for dynamic analysis and predictive modeling. The system's core claim centers around...

Based on Graph Neural Networks for System Security Analysis

This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as gr...

Adaptive Graph Embedding with Relational Context

Graph embedding techniques have become increasingly prevalent in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, traditional graph embedding methods often treat nodes and edges independently, neglecting the crucial relational context that governs the s...

Cognitive Computing with Symbolic Simulation of Human Reasoning

This paper proposes a novel approach to cognitive computing centered around the symbolic simulation of human reasoning. The core idea is to build cognitive systems by explicitly modeling the underlying processes of human thought, leveraging constraints, contextual knowledge, and probabilistic inference. We introduce a...

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...