EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation

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.

[1]  Christos Faloutsos,et al.  EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[2]  Christos Faloutsos,et al.  A General Suspiciousness Metric for Dense Blocks in Multimodal Data , 2015, 2015 IEEE International Conference on Data Mining.

[3]  Christos Faloutsos,et al.  M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees , 2016, ECML/PKDD.

[4]  Danai Koutra,et al.  Graph based anomaly detection and description: a survey , 2014, Data Mining and Knowledge Discovery.

[5]  Danai Koutra,et al.  DeltaCon: Principled Massive-Graph Similarity Function with Attribution , 2016, ACM Trans. Knowl. Discov. Data.

[6]  Christos Faloutsos,et al.  DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams , 2017, KDD.

[7]  LimYongsub,et al.  Memory-Efficient and Accurate Sampling for Counting Local Triangles in Graph Streams , 2018 .

[8]  Sudipto Guha,et al.  SpotLight: Detecting Anomalies in Streaming Graphs , 2018, KDD.

[9]  Chang Zhou,et al.  Toward continuous pattern detection over evolving large graph with snapshot isolation , 2015, The VLDB Journal.

[10]  Jimeng Sun,et al.  Beyond streams and graphs: dynamic tensor analysis , 2006, KDD '06.

[11]  Christos Faloutsos,et al.  D-Cube: Dense-Block Detection in Terabyte-Scale Tensors , 2017, WSDM.

[12]  Christos Faloutsos,et al.  HoloScope: Topology-and-Spike Aware Fraud Detection , 2017, CIKM.

[13]  Hyun Ah Song,et al.  FRAUDAR: Bounding Graph Fraud in the Face of Camouflage , 2016, KDD.

[14]  Yaohang Li,et al.  Single-Pass PCA of Large High-Dimensional Data , 2017, IJCAI.

[15]  Sudipto Guha,et al.  Robust Random Cut Forest Based Anomaly Detection on Streams , 2016, ICML.

[16]  Nathan Halko,et al.  Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..