HGOD: Outlier detection based on a hybrid graph

Abstract

Outlier detection is one of the core problems in the field of data mining. Graph-based approaches are widely acknowledged for their robust performance in outlier detection, yet constructing a comprehensive graph to represent the original data remains challenging. Some methods rely solely on the global distribution of the data, leading to low detection accuracy; in addition, when incorporating the local information of the data, the problem of dangling links may arise. To improve the graph construction process, this paper proposes an outlier detection algorithm based on a hybrid graph (HGOD). First, the neighbors of each object are obtained in an adaptive manner, and a neighborhood graph is created. Subsequently, a global connectivity graph is constructed using a minimum spanning tree. Then, these two graphs are merged into a hybrid graph, and Markov random walks are conducted on it. Finally, outliers are identified based on the converged stationary distribution. The performance of HGOD is evaluated on both real-world and synthetic datasets. The precision and AUC of HGOD algorithm surpasses that of the other seven algorithms. Ablation and analysis experiments further demonstrate that the HGOD algorithm is effective and has outstanding performance in outlier detection.