I/O-Efficient Algorithms for Degeneracy Computation on Massive Networks - Li, Rong-Hua; Song, Qiushuo; Xiao, Xiaokui; Qin, Lu; Wang, Guoren; Yu, Jeffrey Xu; Mao, Rui (IEEE transactions on knowledge and data engineering, 2022)
Degeneracy is an important concept to measure the sparsity of a graph which has been widely used in many network analysis applications. Many network analysis algorithms, such as clique enumeration and truss decomposition, perform very well in graphs having small degeneracies. In this paper, we propose an I/O-efficient algorithm to compute the degeneracy of the massive graph that cannot be fully kept in the main memory. The proposed algorithm only uses O(n) O(n)<inline-graphic xlink:href="wang-ieq1-3021484.gif"/> memory, where n n<inline-graphic xlink:href="wang-ieq2-3021484.gif"/> denotes the number of nodes of the graph. We also develop an I/O-efficient algorithm to incrementally maintain the degeneracy on dynamic graphs. Extensive experiments show that our algorithms significantly outperform the state-of-the-art degeneracy computation algorithms in terms of both running time and I/O costs. The results also demonstrate high scalability of the proposed algorithms. For example, in a real-world web graph with 930 million nodes and 13.3 billion edges, the proposed algorithm takes only 633 seconds and uses less than 4.5GB memory to compute the degeneracy.