Integral Densest Subgraph Search on Directed Graphs

The densest subgraph (DS) search over a directed graph focuses on finding the subgraph with the highest density among all subgraphs. This problem has raised numerous applications, such as fraud detection and community detection. The state-of-the-art DS algorithms have prohibitively high costs or poor approximation ratios, making them unsuitable for practical applications. To address these dilemmas, in this paper, we propose a novel model called integral densest subgraph (IDS). We show that IDS can serve as a near-DS model that has a tight floor relationship with the density of the DS. To compute IDS, we first propose a novel flow network named (α,β)-dense network, based on which we design an exact network-flow algorithm GetIDS with O(p • log |V| • |E| 1.5 ) time complexity, where p is typically a small constant in real-world graphs. Additionally, we propose several non-trivial pruning techniques to further improve the efficiency. Subsequently, we propose a novel (2 + ε)-approximation algorithm MultiCore with near-linear time complexity, providing a good approximation guarantee with high efficiency. Finally, our extensive experiments on 10 real-world graphs demonstrate the effectiveness of the proposed IDS model, and the high efficiency and scalability of the proposed solutions.