EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation

  • Jiabao Zhang
  • Shenghua LiuEmail author
  • Wenjian YuEmail author
  • Wenjie Feng
  • Xueqi Cheng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)


How can we spot dense blocks in a large streaming graph efficiently? Anomalies such as fraudulent attacks, spamming, and DDoS attacks, can create dense blocks in a short time window, emerging a surge of density in a streaming graph. However, most existing methods detect dense blocks in a static graph or a snapshot of dynamic graphs, which need to inefficiently rerun the algorithms for a streaming graph. Moreover, some works on streaming graphs are either consuming much time on updating algorithm for every incoming edge, or spotting the whole snapshot of a graph instead of the attacking sub-block.

Therefore, we propose a row-augmented matrix with sliding window to model a streaming graph, and design the AugSVD algorithm for computation- and memory-efficient singular decomposition. EigenPulse is then proposed to spot the density surges in streaming graphs based on the singular spectrum. We theoretically analyze the robustness of our method. Experiments on real datasets with injections show our performance and efficiency compared with the state-of-the-art baseline.


Surge detection Streaming graphs Sliding window 



This material is based upon work supported by the Strategic Priority Research Program of CAS (XDA19020400), NSF of China (61772498, 61872206, 61425016, 91746301), and the Beijing NSF (4172059).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CAS Key Laboratory of Network Data Science and Technology, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.BNRist, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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