Due to diverse graph data and algorithms, programming and orchestration of complex computation pipelines have become the major challenges to making use of graph applications for Web-scale data analysis. GraphScope aims to provide a one-stop and efficient solution for a wide range of graph computations at scale. It extends previous systems by offering a unified and high-level programming interface and allowing the seamless integration of specialized graph engines in a general data-parallel computing environment. As we will show in this demo, GraphScope enables developers to write sequential graph programs in Python and provides automatic parallel execution on a cluster. This further allows GraphScope to seamlessly integrate with existing data processing systems in PyData ecosystem. To validate GraphScope's efficiency, we will compare a complex, multi-staged processing pipeline for a real-life fraud detection task with a manually assembled implementation comprising multiple systems. GraphScope achieves a 2.86× speedup on a trillion-scale graph in real production at Alibaba.

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills. Although existing libraries and tools reduce the difficulty of generating graph visualization, there are still many challenges. We work closely with developers and formulate several design goals, then design and implement G6, a web-based library for graph visualization. It combines template-based configuration for high usability and flexible customization for high expressiveness. To enhance development efficiency, G6 proposes a range of optimizations, including state management and interaction modes. We demonstrate its capabilities through an extensive gallery, a quantitative performance evaluation, and an expert interview. G6 was first released in 2017 and has been iterated for 317 versions. It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.