Recent advances in dynamic graph processing have enabled the analysis of highly dynamic graphs with change at rates as high as millions of edge changes per second. Solutions in this domain, however, have been demonstrated only for relatively simple algorithms like PageRank, breadth-first search, and connected components. Expanding beyond this, we explore the maximum flow problem, a fundamental, yet more complex problem, in graph analytics. We propose a novel, distributed algorithm for max-flow on dynamic graphs, and implement it on top of an asynchronous vertex-centric abstraction. We show that our algorithm can process both additions and deletions of vertices and edges efficiently at scale on fast-evolving graphs, and provide a comprehensive analysis by evaluating, in addition to throughput, two criteria that are important when applied to real-world problems: result latency and solution stability.

翻译：动态图处理的最新进展使得能够分析变化速率高达每秒数百万条边变化的高度动态图。然而，该领域中的解决方案仅针对相对简单的算法（如PageRank、广度优先搜索和连通分量）得到了验证。为突破这一局限，我们探索了最大流问题——图分析中一个基础但更为复杂的问题。我们提出了一种新颖的分布式算法，用于动态图上的最大流，并在异步顶点为中心的抽象层之上实现了该算法。我们证明，该算法能够在快速演化的图上大规模高效地处理顶点和边的增删操作，并通过评估除了吞吐量之外的两个对实际应用至关重要的指标——结果延迟与解稳定性——提供了全面的分析。