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 ...

“Directed self-assembly (DSA) of block copolymers (BCPs) has long been included in the semiconductor roadmap as a lithographic pathway to enable continued device scaling. Tremendous progress has been ...

Currently, the repository includes only a few topics (e.g., Sorting, Searching, Array, LinkedList, Tree). To make it more comprehensive, we need to add new core DSA topics along with basic ...

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 ...

Abstract: This paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals ...

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...

Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. Jul 03, 2025, 10:43am EDT Business 3d tablet virtual growth ...

Welcome to the most comprehensive Java Data Structures and Algorithms course designed to transform complete beginners into expert problem solvers on platforms like LeetCode, HackerRank, and coding ...

What if you could transform vast amounts of unstructured text into a living, breathing map of knowledge—one that not only organizes information but reveals hidden connections you never knew existed?