Abstract
Commute time is a robust measure on graphs based on random walks. It has been successfully applied in many application domains including personalized search, collaborative filtering and network intrusion detection. However, the computation of the commute time is expensive since it involves the eigen decomposition of the graph Laplacian matrix. There has been effort to approximate the commute time but they only work in an offline mode. In this work, an accurate and efficient approximation for computing the commute time is proposed in an incremental fashion in order to facilitate online applications. Using the incremental commutime, we design an online anomaly detection application where the commute time of each new arriving data point to any point in the current graph can be estimated in constant time. The proposed approach shows its high accuracy and efficiency in synthetic and real datasets for online applications. It takes only 8 milliseconds on average to detect anomalies online on the DBLP graph which has more than 600,000 nodes and 2 millions edges. We also discuss the use of incremental commute time for other online applications such as classification, graph ranking and clustering.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
