graph neural networks

Graph neural networks for network intrusion detection systems: a comprehensive survey, taxonomy, and empirical evaluation

Graph Neural Networks with Intrinsic Dimensionality Reduction

Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommender systems. However, their performance is frequently hampered by the "curse of dimensionality," a fundamental challenge arising from the high-dimensional nature of graph...

Causal Inference Network for Time Series Prediction

Traditional time series prediction models often rely on correlation-based approaches, failing to account for the underlying causal relationships that drive the observed patterns. This paper introduces a novel framework, the Causal Inference Network (CIN), designed to address this limitation. The CIN constructs a networ...

Graph Neural Network-Based Program Vulnerability Detection (GNNVVD)

This paper presents a novel approach to program vulnerability detection utilizing Graph Neural Networks (GNNs). Traditional vulnerability detection methods often struggle with the complex and intricate relationships within software code, leading to missed vulnerabilities and false positives. Our proposed method, Graph...

Causal Inference via Graph Neural Networks with Directional Edges

This paper proposes a novel approach to causal inference leveraging Graph Neural Networks (GNNs) with explicitly modeled directional edges. Traditional GNNs primarily focus on learning representations based on pairwise relationships, often leading to spurious correlations. To address this, we introduce a modification t...

A dynamic recommendation algorithm for regional industry-adapted higher vocational courses integrating counterfactual causal inference and graph neural networks

The mismatch between higher vocational curricula and the shifting skill demands of regional industries continues to constrain graduate employability, while existing recommendation engines remain trapped in correlational logic and selection-biased enrollment histories. This paper proposes CCIG-DRec, a dynamic course rec...

Cognitive Graph Distillation

This paper introduces Cognitive Graph Distillation, a novel approach for extracting high-level cognitive representations from complex neural networks. The core idea is that the intricate dynamics within large neural networks can be effectively summarized by identifying and prioritizing key nodes and connections, result...

#基于认知图谱的知识发现与推理

This paper proposes a novel approach to knowledge discovery and reasoning, termed Cognitive Graph-Based Knowledge Discovery & Reasoning (CGKR). The core idea is to construct a dynamic cognitive graph and leverage its reasoning capabilities alongside those of traditional knowledge graphs. The system integrates the struc...

Learning Generalized Hessian on Graphs for Continuous Graph Learning

Abstract In this work, we propose HbInOut (Hessian-based Influx Outflux), a continuous graph neural network for graph learning and graph ensemble (snapshot) learning. HbInOut models diffusion on graphs by incorporating flow -based priors in its forward-pass construction and includes directionality through learnable inf...

Title: Non-Euclidean Neural Network Architectures for Generative Modeling

This paper investigates the potential of non-Euclidean neural network architectures for enhanced generative modeling. Traditional neural networks, predicated on linear transformations and gradient descent, often struggle to capture the intricate patterns and high-dimensional space inherent in data. We propose a novel s...

Temporal Information Flow Analysis for Machine Learning Models

This paper presents a novel methodology for analyzing the temporal flow of information within neural networks, focusing on identifying bottlenecks and enhancing model stability. Traditional approaches often treat models as static entities, failing to capture the dynamic nature of information propagation. We introduce a...

Unsupervised deep learning framework for early detection of wellbore trajectory deviation in drilling operations

Wellbore trajectory deviation remains one of the major operational challenges encountered during directional and extended-reach drilling because even small departures from the planned well path can lead to poor reservoir placement, wellbore instability, increased non-productive time, and significant drilling costs. In...