Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

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 ...

Neo4j also trumpeted the value of graphs as vector databases used in generative artificial intelligence. AI training requires ...

Recently, Tianjin Yingjie Technology Development Co., Ltd. announced the application for a patent titled "An Optimization Method for Multi-Robot Collaborative Task Scheduling Based on Graph Neural ...

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...

Recently, Financial界 reported that Suzhou Goldfinch Network Information Technology Co., Ltd. has applied for a patent titled 'A User Voice Call System and Method Based on Big Data Processing', with ...

In the first part of the paper several measures for the complexity of networks (graphs and digraphs) are proposed and some of their properties are investigated. The second part of the paper is devoted ...