Outlier detection algorithms are widely used in various fields such as network intrusion detection, and medical aided diagnosis. Local Distance-Based Outlier Factor (LDOF), Cohesiveness-Based Outlier Factor (CBOF) and Local Outlier Factor (LOF) algorithms are classic algorithms for outlier detection with long execution time and low detection rate on large-scale datasets and high dimensional datasets. Aiming at these problems, an outlier detection algorithm Based on Graph Random Walk (BGRW) was proposed. Firstly, the iterations, damping factor and outlier degree for every object in the dataset were initialized. Then, the transition probability of the rambler between objects was deduced based on the Euclidean distance between the objects. And the outlier degree of every object in the dataset was calculated by iteration. Finally, the objects with highest outlier degree were output as outliers. On UCI （University of California， Irvine） real datasets and synthetic datasets with complex distribution, comparison between BGRW and LDOF, CBOF, LOF algorithms about detection rate, execution time and false positive rate were carried out. The experimental results show that BGRW is able to decrease execution time and false positive rate, and has higher detection rate.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019101708