Inferring Strange Behavior from Connectivity Pattern in Social Networks

  • Meng Jiang
  • Peng Cui
  • Alex Beutel
  • Christos Faloutsos
  • Shiqiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)


Given a multimillion-node social network, how can we summarize connectivity pattern from the data, and how can we find unexpected user behavior? In this paper we study a complete graph from a large who-follows-whom network and spot lockstep behavior that large groups of followers connect to the same groups of followees. Our first contribution is that we study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep. We discover that (a) the lockstep behavior on the graph shapes dense “block” in its adjacency matrix and creates “ray” in spectral subspaces, and (b) partially overlapping of the behavior shapes “staircase” in the matrix and creates “pearl” in the subspaces. The second contribution is that we provide a fast algorithm, using the discovery as a guide for practitioners, to detect users who offer the lockstep behavior. We demonstrate that our approach is effective on both synthetic and real data.


Adjacency Matrix Singular Vector Spectral Cluster Connectivity Pattern Social Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Meng Jiang
    • 1
  • Peng Cui
    • 1
  • Alex Beutel
    • 2
  • Christos Faloutsos
    • 2
  • Shiqiang Yang
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Computer Science DepartmentCarnegie Mellon UniversityUSA

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