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1,893 papers
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and knowledge graph reasoning. However, standard GNNs often struggle to effectively capture intricate relational dependencies between nodes. This work introduces a novel approach that...
Predicting protein function and understanding protein-protein interactions (PPIs) are central challenges in modern biology. Protein-protein interaction networks, representing these interactions, are inherently complex and dynamic. Traditional methods often struggle to capture the temporal evolution of these networks, f...
This paper investigates a novel approach to solving non-linear equation systems using graph neural networks (GNNs). Traditional methods often struggle with complex systems, requiring significant computational resources. We propose a method that leverages GNNs to transform the equation into a graph representation, allow...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and knowledge graph reasoning. However, standard GNNs often struggle to effectively capture intricate relational dependencies between nodes. This work introduces a novel approach that...
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This paper introduces Epistemic Graph Neural Networks (EGNNs), a novel approach to graph neural networks that explicitly models and incorporates uncertainty into the learning process. Traditional GNNs primarily focus on learning pattern recognition from graph-structured data, often neglecting the inherent uncertainty a...
Memoria.ia v1.0.0-rc5 — Native Relational Memory Candidate Release date: 2026-09-06 Summary v1.0.0-rc5 freezes the post-RC4 native relational-memory work for OFF.IA, server and mobile validation. Functional freeze commit: 06c747478e05ee11ab2c5c3c24cf75365262b872 No new runtime features are to be added to the RC5 line a...
This paper introduces Contextualized Semantic Graph Networks (CSGN), a novel approach to knowledge representation and reasoning that addresses the limitations of traditional static knowledge graphs. The core idea is that semantic understanding fundamentally relies on a graph representation that dynamically adapts to th...
Adaptive Self-Design Algorithm is a novel algorithmic framework designed to automate the design and optimization of mathematical formulas, particularly within complex non-standard and nonlinear equation systems. This approach leverages a probabilistic search algorithm, incorporating reinforcement learning and genetic a...
Network intrusion detection systems (NIDS) face increasing challenges due to the dynamic and evolving nature of cyber threats. Traditional graph neural network (GNN) approaches often struggle to adapt effectively to these changes, relying on static graph structures and pre-defined node features. This paper proposes a n...
This paper introduces a novel approach to Graph Neural Networks (GNNs) that addresses a critical limitation of traditional GNNs – their inability to effectively model the dynamic evolution of graph structures. We propose Dynamic Graph Neural Networks with Contextual Memory Encoding (DGMNE), a framework that integrates...
This paper explores the application of Graph Neural Networks (GNNs) for causal discovery directly from observational data. Traditional causal inference methods often rely on strong assumptions regarding underlying causal mechanisms, which can be difficult to validate. We propose a novel framework where a GNN learns cau...
Autonomous robot navigation necessitates robust perception and decision-making capabilities that can adapt to dynamic and unpredictable environments. Traditional graph neural networks (GNNs) often rely on static graph structures, failing to effectively represent and reason about the constantly evolving relationships wi...
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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