Chinese and Singaporean researchers have developed a defense mechanism that poisons proprietary knowledge graph data, making ...

Researchers and artificial intelligence work together to collect experimental materials science data from papers worldwide ...

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released learnable quantum spectral filter technology for hybrid graph neural networks. This ...

Author Shawn Peters blends clarity and rigor to make data structures and algorithms accessible to all learners. COLORADO, CO, UNITED STATES, January 2, 2026 /EINPresswire.com/ — Vibrant Publishers ...

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...

Abstract: Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on ...

Abstract: The emerging field of graph learning, which aims to learn reasonable graph structures from data, plays a vital role in Graph Signal Processing (GSP) and finds applications in various data ...

Graphs and data visualizations are all around us—charting our steps, our election results, our favorite sports teams’ stats, and trends across our world. But too often, people glance at a graph ...

I co-created Graph Neural Networks while at Stanford. I recognized early on that this technology was incredibly powerful. Every data point, every observation, every piece of knowledge doesn’t exist in ...

Data‑analysis workflow. Experimental and computational datasets are unified; crystal‑structure graphs, deep learning, and dimensionality reduction yield the materials map. Selecting the right material ...