AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
127 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper explores a novel approach to program generation and explanation by integrating the strengths of neural networks and neuro-symbolic reasoning. Current program generation techniques often fall short due to a lack of explicit logical reasoning and interpretability. We propose a framework that combines a neural...
This paper introduces a novel deep learning model designed for analyzing complex systems by directly processing topological data. The core concept revolves around representing complex data as graphs and leveraging Graph Neural Networks (GNNs) for deep learning. Traditional deep learning methods often struggle with non-...
This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local an...
Existing graph embedding techniques primarily focus on static graph representations, often neglecting the crucial aspect of temporal dynamics inherent in many real-world graphs. This paper introduces a novel approach combining relational graph embeddings with Temporal Graph Neural Networks (TGNNs) to address this limit...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This p...
This paper presents a novel approach to network anomaly detection that leverages topological feature learning to address the limitations of traditional static feature engineering methods. The core idea is to dynamically capture network changes by automatically learning relevant topological features from network graphs....
Graph representation learning has emerged as a crucial technique in various domains, including social network analysis, drug discovery, and recommendation systems. However, many existing methods struggle to effectively capture long-range dependencies within graphs, leading to suboptimal representations. This paper intr...
Traditional methods for causal discovery often face challenges when dealing with intricate, high-dimensional datasets, frequently failing to accurately capture complex causal relationships. Furthermore, these methods typically lack the capability to simulate the consequences of interventions, hindering the ability to v...
The Semantic Web, characterized by linked data and intricate relationships between entities, presents significant challenges for traditional database systems. Existing graph databases often struggle to effectively perform complex reasoning tasks over this data. This paper proposes a novel approach utilizing Graph Neura...
This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local an...
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and modeling complex relationships within graphs, finding applications in diverse fields like social network analysis, drug discovery, and computer vision. However, traditional GNN architectures often suffer from limitations in capturing long-ra...
This paper explores a novel approach to knowledge graph reasoning utilizing Graph Neural Networks (GNNs). Traditional knowledge graph reasoning methods often struggle with scalability and accurately capturing complex relationships within vast knowledge bases. This work proposes a framework that represents knowledge gra...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.