Graph AI carries immense potential for us to explore, connect the dots and build intelligent applications using the Internet of Behaviors (IoB). Many Graph Neural Networks achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their area to the students. The purpose of this course is to unfold the basics to the cutting-edge concepts and technologies in this realm. Graphs are all around us; real-world objects are often defined in terms of their connections to other things.

"Graph algorithms are the driving force behind the next generation of AI and machine learning that will power even more industries and use cases," …

Graph AI Summit is the first and only open conference for accelerating analytics, AI and machine learning with graph algorithms.

Lynx Analytics announces the open source release of its Complete Graph Data Science Platform, LynxKite 4.0, after years of development and successful deployments with customers. With rapidly growing availability of network and relationship data as well as new graph deep learning technologies, Graph AI is the next frontier of machine learning as advocated by leading machine learning experts. By integrating relationship information into machine learning models, graphs are a crucial component in numerous AI applications: network based attribute prediction, fraud detection, product recommendation, infrastructure and operations optimization, drug discovery, etc. Up until today, building Graph AI solutions has been a highly technical process that involved numerous skills, tools and coding efforts. This has created a high barrier to entry and slow adoption of Graph Analytics.