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Individual and Collective Graph Mining

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13 December 2020


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Individual and Collective Graph Mining: Principles, Algorithms and Applications

Authors: Danai Koutra, Christos Faloutsos

Link: https://www.morganclaypool.com/doi/10.2200/S00796ED1V01Y201708DMK014

Keywords: data mining, graph mining and exploration, graph similarity, graph matching, network alignment, graph summarization, pattern mining, outlier detection, anomaly detection, scalability, fast algorithms, visualization, social networks, brain graphs, connectomes

Morgan-Claypool Book

Citation (bibtex):

@book{KoutraF17,
  author    = {Danai Koutra and
               Christos Faloutsos},
  title     = {Individual and Collective Graph Mining: Principles, Algorithms and Applications},
  publisher = {Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool}, 
  year      = {2017},
  pages     = {206}
  }

Chapter 2: Summarization of Static Graphs

VoG code (Matlab / Python)

Slides

Chapter 3: Inference in a Graph

Two Classes

FaBP code (Matlab)

Slides

Multiple Classes

mFaBP code (Python)

mFaBP code (SQL)

More detailed description & derivations

Full paper with all the proofs

Slides

Video

Chapter 4: Summarization of Dynamic Graphs

TimeCrunch code (Matlab / Python)

Chapter 5: Graph Similarity

DeltaCon code (Matlab)

DeltaCon code (R)

Slides

Tutorial slides at SDM’14 & ICDM’14

Chapter 6: Graph Alignment

Slides

Tutorial slides at SDM’14 & ICDM’14


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