Estimating Clustering Coefficient via Random Walk on MapReduce - Liao, Qun; Yang, Yulu (2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), 2017)
Clustering coefficient plays an important role in many real-world applications, such as social network analysis and community mining. However, it is expensive to compute exact clustering coefficient for large networks. In many real-world applications, estimating clustering coefficient via random walk method is preferable because it is efficient and accurate. MapReduce is a popular distributed programming framework for processing large datasets. In this paper, we propose an algorithm on MapReduce framework to estimate clustering coefficient based on random walk method. Experiments on a Hadoop cluster for large real-world graphs demonstrate that the proposed algorithm is accurate and efficient. Comparing to Doubling algorithm, a state-of-the-art distributed algorithm to implement random walk on MapReduce, the proposed algorithm runs much faster. The proposed algorithm also reduces I/O cost efficiently.