Dynamic Adaptive Graph Neural Networks (D-AGNNs) represent a significant advancement in graph neural network design, focusing on the ability to dynamically adjust the graph structure to optimize both representation and reasoning capabilities. This paper introduces a novel algorithmic framework, leveraging adaptive algo...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel machine learning algorithm, termed the Dynamic Graph-Based Machine Learning Algorithm, designed to enhance the performance of graph neural networks (GNNs) through adaptive graph structure learning. Traditional GNN approaches often rely on pre-defined graph structures, limiting their applic...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
SA-Net, a dual-branch CNN–GNN Network that integrates synergistic attention and frequency-domain modulation, provides accurate and robust segmentation performance under challenging ultrasound imaging conditions.
Xi-Cheng Fu, Jing-Yao Lei, Qiang Wang et al.· Journal of Electronic Imagin...· 0 citations
This paper introduces a novel self-adaptive learning method for graph neural networks (GNNs). Traditional GNNs often rely on fixed architectures, limiting their ability to adapt to evolving data landscapes. We propose a method that dynamically adjusts the graph structure of the network during training, leveraging the i...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper introduces a novel algorithm for graph neural network (GNN) graph structure adjustment, leveraging the principles of adaptive neural networks. Traditional approaches often struggle with fixed parameter settings, limiting the model's ability to effectively capture complex relationships within the data. Our pr...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of Graph Neural Networks (GNNs) for automated software dependency discovery. Traditional methods for identifying dependencies rely heavily on manual annotation or rule-based approaches, often proving insufficient in complex software systems. We propose a novel approach that leverages...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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...
Bin Zhang, Weiliang Ma, 康振忠 et al.· Zenodo (CERN European Organi...· 0 citations
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...
Minwei Zhao, S. Šćepanović, Stephen Law et al.· Computers, Environment and U...· 1 citation
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...
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.
Microsoft Research Blog· microsoft.comJul 13, 2026
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.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
