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1,934 papers
This paper introduces an Adaptive Graph Neural Network (AGNN) designed to leverage geometric information for enhanced feature extraction and learning. Traditional graph neural networks (GNNs) often rely solely on data, limiting their ability to exploit the inherent structure of the data. This research proposes a novel...
This paper proposes a novel approach to system security analysis leveraging Graph Neural Networks (GNNs). Traditional security assessments often rely on static analysis and may fail to capture the dynamic and interconnected nature of complex systems. We argue that system vulnerabilities can be effectively modeled as gr...
This paper proposes a novel framework for modeling social networks based on the principles of empathetic computation. Traditional social network models primarily focus on structural aspects and relationships, often neglecting the crucial role of emotions and behaviors in shaping social interactions. This research intro...
This paper introduces a novel approach to multimodal data fusion utilizing graph-to-graph models. The core idea is to represent diverse data modalities—including text, images, and audio—as interconnected nodes within a graph structure. This graph facilitates the fusion of information and enables reasoning across modali...
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This work develops the principle that choosing a neural architecture is not a matter of choosing the largest model, but of choosing an inductive bias that matches the structure the data already has through three self-contained case studies.
This paper proposes a novel approach to software component recommendation utilizing dynamic graph neural networks (DGNNs). Traditional software component recommendation methods often rely on static graphs or keyword-based searches, which fail to effectively capture the dynamic relationships and usage patterns inherent...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, recommendation systems, and molecular property prediction. However, a key limitation of many GNN architectures lies in their static neighborhood aggregation strategy, often relying on fixed neighborh...
This paper explores the application of Hierarchical Graph Neural Networks (HGN) to modeling and understanding complex systems. Traditional graph neural networks often treat the entire graph as a single entity, limiting their ability to capture the inherent hierarchical structure and emergent properties of these systems...
This paper investigates the application of Quantum Adaptive Algorithms (QAA) to Graph Neural Networks (GNNs). We propose a novel QAGAN architecture that leverages quantum self-optimization to enhance the training and inference process of GNNs. The core of this approach is a dynamically adjusted parameter set derived fr...
This paper proposes a novel framework for constructing and reasoning with dynamic semantic graph knowledge. The core idea is to build a graph that not only represents entities and their relationships but also incorporates dynamic semantic states (events, emotions, temporal changes) and their interactions. This dynamic...
This paper explores the application of Graph Neural Networks (GNNs) to social network analysis. Traditional social network analysis methods often struggle to capture complex, hidden relationships and patterns within networks, particularly in large and dynamic social structures. GNNs offer a novel approach by directly l...
This paper investigates the development of a novel data structure auto-generation system utilizing machine learning. Recognizing the challenges posed by high-dimensional data, this research proposes a method for automatically constructing data structures like matrices and graphs, thereby simplifying data processing and...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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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