Common Neighbors Matter: Fast Random Walk Sampling With Common Neighbor Awareness

Random walk is widely applied to sample large-scale graphs due to its simplicity of implementation and solid theoretical foundations of bias analysis. However, its computational efficiency is heavily limited by the slow convergence rate (a.k.a. long burn-in period). To address this issue, we propose a common neighbor aware random walk framework called CNARW, which leverages weighted walking by differentiating the next-hop candidate nodes to speed up the convergence. Specifically, CNARW takes into consideration the common neighbors between previously visited nodes and next-hop candidate nodes in each walking step. Based on CNARW, we further develop two efficient unbiased sampling schemes, and we also design two variant algorithms which can reduce sampling cost and speed up the convergence. Experimental results on real-world network datasets show that our approach converges remarkably faster than the state-of-the-art random walk sampling algorithms; and to achieve the same estimation accuracy, our approach reduces the query cost significantly. Last, we use two case studies to demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.

Automated summary

Random walk is a popular method for sampling large-scale graphs, but it suffers from slow convergence. To address this issue, we propose a common neighbor aware random walk framework called CNARW, which speeds up convergence by considering common neighbors. We also design efficient unbiased sampling schemes and variant algorithms to reduce cost and improve convergence. Experimental results show that our approach outperforms state-of-the-art random walk sampling algorithms in terms of convergence speed and query cost reduction. Two case studies demonstrate the effectiveness of our sampling framework in solving large-scale graph analysis tasks.