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1,934 papers
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...
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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...
This paper introduces a novel dynamic topology optimization algorithm based on graph neural networks (GNNs). Traditional topology optimization methods often rely on manual design, struggling to adapt to complex systems exhibiting dynamic changes. Graph Neural Networks (GNNs) offer a powerful framework for automatically...
This paper proposes a novel network computation model termed "Space-Time Network Computation" (STNC). The core claim is that integrating spatial and temporal dimensions offers a more efficient approach to processing data exhibiting spatial and temporal dependencies. STNC leverages an extension of Graph Neural Networks...
This paper introduces a novel dynamic adaptive graph neural network (DAGNN) architecture designed to optimize network performance through automated structural adjustments. Traditional graph neural networks often rely on fixed, pre-defined structures, limiting their adaptability to diverse data. Our approach leverages a...
Predicting protein-protein interactions (PPIs) is a fundamental challenge in bioinformatics with significant implications for understanding biological systems and drug discovery. Traditional methods often struggle to capture the dynamic nature of PPIs, which are not static events but rather fluctuate over time due to v...
Hundreds of millions of people interact with language models (LMs) every day, using them for tasks such as writing assistance and information seeking. As their range of applications grows, it becomes increasingly essential to consider the role of social context in how these systems are designed and deployed. In particu...
This paper presents a novel approach to complex system modeling leveraging the power of graph neural networks (GNNs). We construct a model designed to predict system behavior and states by effectively capturing relationships between nodes and edges within the system. The core mechanism of this approach is the utilizati...
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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