Learning Features of Brain Network for Anomaly Detection

Brain network is a kind of biological networks, which could express complex connectivity among brain functional components. The node in brain network denotes region of interest, which executes specific function in the brain. The edge in brain network represents the connection relationship between nodes. Neuropsychiatric disorders can cause changes in the brain's nerves, which will further change the related characteristics of brain network. The detection for neuropsychiatric disorders with brain network could be treated as an anomaly detection problem. Recent researches have explored using complex networks or graph mining to deal with the detection problem. However, they neither ignore local structural features in critical regions nor fail to comprehensively extract the structural features for brain network. In this paper, we propose a feature learning method to build effective representations for brain network. By treating the closed frequent graph as a node, these representations contain both connection relationships in critical regions and local/global structural features for critical regions, which could benefit the detection with brain network. Experiments using real world data indicate that the proposed method could improve the detection ability of existing machine learning methods in the literatures.