A Fast Data Structure for Dynamic Graphs Based on Hash-Indexed Adjacency Blocks

Several dynamic graph data structures have been proposed in literature. Yet, these data structures either offer limited support for arbitrary graph algorithms or they are designed as part of specific frameworks (e.g., for GPUs or specialized hardware). Such frameworks are difficult to adopt to arbitrary graph computations and lead practitioners to fall back to less sophisticated solutions when dealing with dynamic graphs. In this work, we propose a new “dynamic hashed blocks” (DHB) data structure for sparse dynamic graphs and matrices on general-purpose CPU architectures. DHB combines an efficient block-based memory layout to store incident edges with an additional per-vertex hash index for high degree vertices. This hash index allows us to quickly insert edges without introducing duplicates, while the block-based memory layout retains advantageous cache locality properties of traditional adjacency arrays. Experiments show that DHB outperforms competing dynamic graph structures for edge insertions, updates, deletions, and traversal operations. Compared to static CSR layouts, DHB exhibits only a small overhead in traversal performance. DHB’s interface is similar to general-purpose abstract graph data types and can be easily used as a drop-in replacement for traditional adjacency arrays. To demonstrate that, we modify the well-known NetworKit framework to use DHB instead of its own dynamic graph representation. Experiments show that this modification only slightly penalizes the performance of graph algorithms while considerably boosting update rates.