A scale-free structure for power-law graphs

Many real-world graphs, such as those that arise from the web, biology and transportation, appear random and without a structure that can be exploited for performance on modern computer architectures. However, these graphs have a scale-free graph topology that can be leveraged for locality. Existing sparse data formats are not designed to take advantage of this structure. They focus primarily on reducing storage requirements and improving the cost of certain matrix operations for these large data sets. Therefore, we propose a data structure for storing real-world scale-free graphs in a sparse and hierarchical fashion. By maintaining the structure of the graph, we preserve locality in the graph and in the cache. For synthetic scale-free graph data we outperform the state of the art for graphs with up to 10
⁷
non-zero edges.