GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs

• URL: http://arxiv.org/abs/2503.06212v2
• Date: Wed, 02 Apr 2025 20:44:51 GMT
• Title: GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
• Authors: Yue Jin, Yongchao Liu, Chuntao Hong,
• Abstract summary: Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges.<n>Existing solutions face significant trade-offs: online subgraph generation is limited to a single machine, resulting in severe performance bottlenecks.<n>We propose textbfGraphGen+, an integrated framework that synchronizes distributed subgraph generation with in-memory graph learning.
• Score: 9.024357901512928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges. To enable efficient training on such large graphs, mini-batch subgraph sampling is commonly used, which allows training without loading the entire graph into memory. However, existing solutions face significant trade-offs: online subgraph generation, as seen in frameworks like DGL and PyG, is limited to a single machine, resulting in severe performance bottlenecks, while offline precomputed subgraphs, as in GraphGen, improve sampling efficiency but introduce large storage overhead and high I/O costs during training. To address these challenges, we propose \textbf{GraphGen+}, an integrated framework that synchronizes distributed subgraph generation with in-memory graph learning, eliminating the need for external storage while significantly improving efficiency. GraphGen+ achieves a \textbf{27$\times$} speedup in subgraph generation compared to conventional SQL-like methods and a \textbf{1.3$\times$} speedup over GraphGen, supporting training on 1 million nodes per iteration and removing the overhead associated with precomputed subgraphs, making it a scalable and practical solution for industry-scale graph learning.

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