{"width":"100%","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fblog-en.fltech.dev%2Fentry%2F2026%2F08%2F14%2Fgnext-part1\" title=\"Introducing GNext: A Shared Foundation for Graph AI Applications - fltech - Technology Blog of Fujitsu Research\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","version":"1.0","blog_url":"https://blog-en.fltech.dev/","categories":["AI"],"blog_title":"fltech - Technology Blog of Fujitsu Research","author_name":"aparajita-fltech","height":"190","provider_name":"Hatena Blog","description":"GNext is a reusable foundation for building graph-based AI applications from complex multimodal data. In this first release, the Graph AI team from Fujitsu Research of Europe\u2019s AI Lab introduces how GNext connects graph construction, learning, analytics, explainability, provenance, and contribution workflows through a shared GraphIR representation, with example applications in satellite intelligence, personalised medicine, and financial fraud detection. The team is inviting contributors and early adopters to try GNext.","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/a/aparajita-fltech/20260811/20260811205918.png","published":"2026-08-14 19:00:00","author_url":"https://blog.hatena.ne.jp/aparajita-fltech/","type":"rich","title":"Introducing GNext: A Shared Foundation for Graph AI Applications","provider_url":"https://hatena.blog","url":"https://blog-en.fltech.dev/entry/2026/08/14/gnext-part1"}