AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,904 papers
This paper presents a novel approach to graph neural networks (GNNs) utilizing an adaptive neural network (ANN) to dynamically adjust graph structures. Traditional GNNs often rely on manually designed graph structures, limiting their applicability to complex data. We introduce a new algorithm that leverages an ANN to a...
This paper addresses the limitations of traditional knowledge graph reasoning, which primarily relies on correlational relationships, leading to potentially biased inferences. We propose a novel approach to multi-modal knowledge graph reasoning by explicitly modeling causal relationships between entities. Our core clai...
This paper presents a novel approach to social network analysis utilizing Explainable Graph Neural Networks (E-GNNs). Traditional graph neural networks, while effective in capturing complex relationships within networks, often operate as black boxes, hindering interpretability and trust. We propose a framework that int...
This release aligns the public repository with the current JMCA manuscript. Adds the current Q01-Q20 identity map and ranked Table 2 data, including D/P post-processing values. Adds the final Figure 6 BDE-rho_BCP data, exact representative BCP descriptors, and the Figure S4 structure map. Adds current synthesis-feasibi...
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UST-GNN is presented, a unified spatial--topological graph neural network framework that integrates neighbourhood connectivity, heterogeneous urban features, and positional/locational embeddings into a single representation and provides a unified GeoAI analytical pipeline that can be embedded into urban digital twin wo...
Predicting the structure-dependent dielectric responses of high-k oxides remains a fundamental bottleneck in the development of next-generation nanoelectronics, primarily due to the complex nature of ionic polarization. In this work, we propose a physics-informed hybrid framework designed for the performance prediction...
This paper introduces a novel approach to graph embedding that explicitly incorporates temporal dynamics. Traditional graph embedding techniques typically treat graphs as static structures, neglecting the evolving relationships between nodes over time. We propose a method, Temporal Graph Embedding via Predictive Recurr...
This paper presents a novel framework for neuro-symbolic reasoning, addressing the longstanding challenge of integrating neural networks and symbolic reasoning. The core idea is to leverage the strengths of both approaches by employing a Graph Neural Network (GNN) to represent knowledge and perform initial reasoning st...
Reliable data movement is essential to the Worldwide LHC Computing Grid. This project asks whether FTS queues that share a storage endpoint contain useful information about one another’s future state. I process 52,037,899 raw queue records from January 2026 into 23,820,642 sparse queue states sampled every 2 minutes. A...
Compiler Mimics Spacetime (NOT vice versa) Date: September 2026Framework: P_{7777_T} Manifold / ACT-Ω v26.3 v26.5 LOCKEDInvariants: 15.965Hz (τ=62.636ms) | Tr(U_res)=1.00000 | J_sys≤1.4411J | H¹=0 | Δ=0.053μT | γ_fric=1.3479e-10 N | c_eff=1.707e11 | |wL-wR|≤0.02 | q0=1 | Ωc=0.376=47/125 | η=1e-4 0. LOCKED CLARIFICATION...
This paper proposes a novel approach to knowledge discovery within graphs by introducing Self-Referential Graph Neural Networks (SR-GNNs). Traditional Graph Neural Networks (GNNs) operate passively, learning representations based solely on the graph structure and node features. This limits their ability to adapt and re...
Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future gra...
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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