发表机构
Texas A&M University; Lawrence Berkeley National Laboratory; University of Illinois at Urbana-Champaign（德克萨斯农工大学; 劳伦斯伯克利国家实验室; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究网络对齐问题，提出FastAlign框架，它通过保留最优传输公式，结合稀疏感知图计算与特定领域内核融合，在不降低对齐精度的情况下，大幅提升了计算效率，减少了端到端运行时间。
AI 中文摘要
网络对齐可识别不同网络间的节点对应关系，是许多数据科学应用（如社交网络分析、欺诈检测和知识图谱整合）中的基本要素。然而，现有网络对齐方法常通过反复构建和更新密集矩阵来实现高精度，在此过程中牺牲了可扩展性。为在不影响对齐精度的情况下解决可扩展性限制，我们提出了FastAlign，这是一个用于基于最优传输的网络对齐的可扩展、稀疏感知框架。FastAlign保留了原始的最优传输公式，并将其计算重新解释为一组重复的混合稀疏-密集操作。它将稀疏感知图计算与特定领域的内核融合相结合，包括一个自定义的稀疏矩阵乘法内核。我们的结果表明，FastAlign在CPU上可将端到端运行时间大幅减少3.89倍至9.45倍，在GPU上可减少2.24倍至32.54倍，同时实现与基于最优传输的现有方法相当的对齐质量。
英文摘要
Network alignment identifies node correspondences across different networks and is a fundamental primitive in many data science applications, including social network analysis, fraud detection, and knowledge graph integration. However, state-of-the-art network alignment methods often achieve high accuracy by repeatedly constructing and updating dense matrices, sacrificing scalability in the process. To address this scalability limitation without compromising alignment accuracy, we present FastAlign, a scalable, sparsity-aware framework for optimal transport-based network alignment. Rather than introducing a new alignment model, FastAlign preserves the original OT formulation and reinterprets its computation as a set of recurring mixed sparse-dense operations. FastAlign combines sparsity-aware graph computation with domain-specific kernel fusion, including a custom SpMM kernel. Our results show that FastAlign achieves alignment quality comparable to state-of-the-art OT-based methods while substantially reducing end-to-end runtime up to 3.89x-9.45x on CPU and 2.24x-32.54x on GPU.
Comments10 pages, 7 figures. Code available at https://github.com/elawh1/FastAlign
