An Implementation of I/O-Efficient Dynamic Breadth-First Search Using Level-Aligned Hierarchical Clustering

In the past a number of I/O-efficient algorithms were designed to solve a problem on a static data set. However, many data sets like social networks or web graphs change their shape frequently. We provide experimental results of the first external-memory dynamic breadth-first search (BFS) implementation based on earlier theoretical work [13] that crucially relies on a randomized clustering. We refine this approach using a new I/O-efficient deterministic clustering, which groups vertices in a level-aligned hierarchy and facilitates easy access to clusters of changing sizes during the BFS updates. In most cases the new externalmemory dynamic BFS implementation is significantly faster than recomputing the BFS levels after an edge insertion from scratch.