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The industry and academia have proposed many distributed graph processingsystems. However, the existing systems are not friendly enough for users likedata analysts and algorithm engineers. On the one hand, the programing modelsand interfaces differ a lot in the existing systems, leading to high learningcosts and program migration costs. On the other hand, these graph processingsystems are tightly bound to the underlying distributed computing platforms,requiring users to be familiar with distributed computing. To improve theusability of distributed graph processing, we propose a unified distributedgraph programming framework UniGPS. Firstly, we propose a unifiedcross-platform graph programming model VCProg for UniGPS. VCProg hides detailsof distributed computing from users. It is compatible with the popular graphprogramming models Pregel, GAS, and Push-Pull. VCProg programs can be executedby compatible distributed graph processing systems without modification,reducing the learning overheads of users. Secondly, UniGPS supports Python asthe programming language. We propose an interprocess-communication-basedexecution environment isolation mechanism to enable Java/C++-based systems tocall user-defined methods written in Python. The experimental results show thatUniGPS enables users to process big graphs beyond the memory capacity of asingle machine without sacrificing usability. UniGPS shows near-linear datascalability and machine scalability.

UniGPS: A Unified Programming Framework for Distributed Graph Processing