Insights from the Beijing Graph Computing Seminar: Industry‑Academia Collaboration and Future Directions

As a frontier of artificial intelligence, graph computing is entering a golden era of development.

On August 24, a seminar jointly organized by MIT Technology Review China and Ant Group’s research institute was held in Beijing, where experts from academia, industry, and applications shared perspectives on the technical evolution and application outlook of graph computing. CB Insights China presented an upcoming report on Chinese graph computing technology and applications.

Key participants included Zhang Yunquan (Secretary‑General of the High‑Performance Computing Committee of the China Computer Federation), Wang Baohui (Senior Engineer at Beihang University), Tao Si Data founder Tao Jianhui, and representatives from energy and finance sectors, who discussed collaborative models for graph computing research and development.

Ant Group’s graph database lead Hong Chuntao described graphs as a technology that aligns with human thinking and more naturally reflects the world, though it can be more complex for machines to process.

Graph computing abstracts data as graphs, offering richer relationship representation and visualization compared to relational databases, which suits the era of massive, complex data but also introduces technical challenges.

Graph computing is poised to become a new foundational data layer.

Universities and industry are actively collaborating, with academic research focusing on chip‑level distributed storage and analysis architectures, distributed graph platforms, deep integration of graph computing with heuristic algorithms, multimodal data fusion, and the convergence of quantum computing and graph computing.

Industry experts emphasized that academia tends to focus on long‑term scientific impact, while industry seeks short‑term performance results; effective collaboration often involves industry providing concrete scenarios and pain points, which academia then addresses for commercial deployment.

Research hotspots include AI‑driven drug discovery using graph machine learning and pre‑training techniques for large‑scale graph data, which can produce universal models applicable to downstream tasks.

Tao Si Data founder Tao Jianhui highlighted the acceleration of product iteration through open‑source models, citing rapid feedback and community contributions as key benefits.

StarRing Technology’s pre‑sales director Dongfang explained that graph data’s point‑edge representation enables faster, millisecond‑level queries for complex relationship scenarios, making graph databases ideal for such use cases.

CEO Ye Xiaomeng of Oru Data suggested a simple rule: if business relationships involve more than two hops, a graph database is the optimal choice.

Industry challenges include the difficulty of aligning graph technology with diverse business scenarios, the shortage of skilled talent and operations personnel, and the immature ecosystem lacking mature middleware and applications.

Ultipa co‑founder Zhang Jiansong expressed optimism, noting that graph databases have surpassed 100% market growth and will become a mainstream direction as data scales and complexity increase, ultimately supporting the development of strong AI with graph‑based reasoning.

Ant Group’s graph database lead Hong Chuntao emphasized the long‑term promise of graph models as a more natural and expressive data abstraction, already delivering value in risk control, social networking, and recommendation systems, while calling for a richer ecosystem.

CB Insights China previewed the forthcoming "China Graph Computing Technology and Application Development Report," which highlights the natural, intuitive nature of graph data, its rapid adoption driven by deep relationship mining needs, and the doubling of graph database interest over the past five years.

The report notes significant investment in graph database startups, with Neo4j receiving $325 million in a Series F round, and identifies major players such as Ant Group, Alibaba, Tencent, AWS, Neo4j, TigerGraph, and domestic startups.

Despite early commercial stages and limited market awareness, graph computing is expanding beyond finance and energy into broader domains, and future progress depends on stronger academia‑industry linkages, ecosystem development, and diversified application scenarios.

The full report will be released in early September by CB Insights China.