StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs

  • Benjamin Schiller
  • Sven Jager
  • Kay Hamacher
  • Thorsten Strufe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9199)


Determining the occurrence of motifs yields profound insight for many biological systems, like metabolic, protein-protein interaction, and protein structure networks. Meaningful spatial protein-structure motifs include enzyme active sites and ligand-binding sites which are essential for function, shape, and performance of an enzyme. Analyzing their dynamics over time leads to a better understanding of underlying properties and processes. In this work, we present StreaM, a stream-based algorithm for counting undirected 4-vertex motifs in dynamic graphs. We evaluate StreaM against the four predominant approaches from the current state of the art on generated and real-world datasets, a simulation of a highly dynamic enzyme. For this case, we show that StreaM is capable to capture essential molecular protein dynamics and thereby provides a powerful method for evaluating large molecular dynamics trajectories. Compared to related work, our approach achieves speedups of up to 2, 300 times on real-world datasets.


Molecular Dynamic Simulation Root Mean Square Deviation Batch Size Distance Threshold Protein Dynamic 
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 2015

Authors and Affiliations

  • Benjamin Schiller
    • 1
  • Sven Jager
    • 2
  • Kay Hamacher
    • 2
    • 3
  • Thorsten Strufe
    • 1
  1. 1.Privacy and Data Security, Department of Computer ScienceTU DresdenDresdenGermany
  2. 2.Computational Biology and Simulation, Department of BiologyTU DarmstadtDarmstadtGermany
  3. 3.Department of Physics, Department of Computer ScienceTU DarmstadtDarmstadtGermany

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