Abstract
A streaming graph is a constantly growing sequence of directed edges, which provides a promising way to detect valuable information in real time. A bursting pattern in a streaming graph represents some interaction behavior which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Mining bursting pattern is essential to early warning of abnormal or notable event. While Bursting pattern discovery enjoys many interesting real-life applications, existing research on frequent pattern mining fails to consider the bursting features in graphs, and hence, may not suffice to provide a satisfactory solution. In this paper, we are the first to address the continuous bursting pattern discovering problem over the streaming graph. We present an auxiliary data structure called BPD for detecting the burst patterns in real time with a limited memory usage. BPD first converts each subgraph into a sequence, and then map it into corresponding tracks based on hash functions to count its frequency in a fixed time window for finding the bursting pattern. Extensive experiments also confirm that our approach generate high-quality results compared to baseline method.
Published Version
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