Structure2Content: An Incremental Method for Detecting Outlier Correlation in Heterogeneous Network

Abstract

Heterogeneous networks are ubiquitous. People like to discover rare but meaningful objects and patterns from such networks. Regardless of high structure similarity or high content similarity, the corresponding objects can be used in data analysis. However, the vast differences between structure and contents should be paid more attention. In this paper, we propose an outlier correlation detection method, called Structure2Content, which discovers outlier correlation incrementally in structure-level and content-level. Structure2Content addresses three important challenges: (1) how can we measure the target object’s structure and content similarity? (2) how can we find the representative features of target objects? (3) how can we insert new data or delete the obsoleted data incrementally. To tackle these challenges, Structure2Content applies four main techniques: (1) two matrices are used to store structure and content similarity, respectively, (2) 3-tuples are used to represent the closeness degree between objects, (3) a mirror step and an iterative process are combined to obtain the top-K outlier correlations, and (4) only updating 3-tuples can help insert or delete data incrementally instead of training all data from the beginning. Substantial experiments show that our proposed method is very effective for outlier correlations detection.