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Large Graph-Mining - Power Tools and a Practitioner's Guide


21 May 2015

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Large Graph-Mining: Power Tools and a Practitioner’s Guide


How to find patterns in large graphs, spanning Giga and Tera bytes? What are the best tools from matrix algebra, and how can they help us solve graph mining problems? These are exactly the goals of this tutorial. Matrix algebra and graph theory can offer powerful tools and theorems, like SVD, spectral analysis, community detection, and more; we single out the most useful tools, we show the intuition behind them, and we give one or more practical settings that each tool performed well. We also cover the emerging map/reduce architecture, and its impact on large graph mining.

Large Graph-Mining: Power Tools and a Practitioner’s Guide

Christos Faloutsos, Gary L Miller, Charalampos E. Tsourakakis
2 videos

Outline and Foils

All pdfs (1-per-page, 4-per-page, and  6-per-page).
Video of the lecture.

(pdf ) Introduction

(pdf) Task 1: Node importance

  • SVD
  • HITS
  • PageRank

(pfd) Task 2: Community detection

  • METIS, Spectral partitioning
  • co-clustering, cross-associations

(pdf) Task 3: Recommendations

  • Proximity

(pdf) Task 4: Connection sub-graphs

(pdf) Task 5: Mining graphs over time & tensors

  • PARAFAC, Tucker

(pdf) Task 6: Virus/influence propagation

(pdf) Task 7: Spectral Graph Theory: 

  • Properties of Adjacency Matrix, Laplacian, 
  • Sparsest cut and Cheeger Inequality, Normalized Laplacian

(pdf) Task 8: Tera/peta graph mining: Hadoop

(pdf) Conclusions

Target Audience

The target audience are data mining and machine learning professionals who wish to know the most important matrix algebra tools and their applications in large graph mining.


Computer science background (B.Sc or equivalent); familiarity with undergraduate linear algebra.


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