On Anomalous Hotspot Discovery in Graph Streams
Network streams have become ubiquitous in recent years because of many dynamic applications.Such streams may show localized regions of activity and evolution because of anomalous events.This paper will present methods for dynamically determining anomalous hot spots from network streams.These are localized regions of sudden activity or change in the underlying network.We will design a localized principal component analysis algorithm, which can continuously maintain the information about the changes in the different neighborhoods of the network.We will use a fast incremental eigenvector update algorithm based on von Mises iterations in a lazy way in order to efficiently maintain local correlation information.This is used to discover local change hotspots in dynamic streams.We will finally present an experimental study to demonstrate the effectiveness and efficiency of our approach.
graph streams anomaly detection
Weiren Yu Charu C.Aggarwal Shuai Ma Haixun Wang
SKLSDE Lab Beihang University, China IBM Research Yorktown, NY, USA Google Research California, USA
国际会议
昆明
英文
293-300
2014-05-01（万方平台首次上网日期，不代表论文的发表时间）