Reachability in Large Graphs Using Bloom Filters

Abstract

Reachability queries are a fundamental graph operation with applications in several domains. There has been extensive research over several decades on answering reachability queries efficiently using sophisticated index structures. However, most of these methods are built for static graphs. For graphs that are updated very frequently and are massive in size, maintaining such index structures is often infeasible due to a large memory footprint and extremely slow updates. In this paper, we introduce a technique to compute reachability queries for very large and highly dynamic graphs that minimizes the memory footprint and update time. In particular, we enable a previously proposed, index-free, approximate method for reachability called ARROW on a compact graph representation called Bloom graphs. Bloom graphs use collections of the well known summary data structure called the Bloom filter to store the edges of the graph. In our experimental evaluation with real world graph datasets with up to millions of nodes and edges, we show that using ARROW with a Bloom graph achieves memory savings of up to 50%, while having accuracy close to 100% for all graphs.