Concurrent Katz Centrality for Streaming Graphs

Abstract

Most current frameworks for streaming graph analysis “stop the world” and halt ingesting data while updating analysis results. Others add overhead for different forms of version control. In both methods, adding additional analysis kernels adds additional overhead to the entire system. A new formal model of concurrent analysis lets some algorithms, those valid for the model, update results concurrently with data ingest without synchronization. Additional kernels incur very little overhead. Here we present the first experimental results for the new model, considering the performance and result latency of updating Katz centrality on a low-power edge platform. The Katz centrality values remain close to the synchronous algorithm while reducing latency delay from 12.8$\times $ to 179$\times $.