Table of Contents

PyNet

Python URLs

Network analysis using Python

by Vladimir Batagelj

Chapters

  1. Preface
  2. Description of networks
  3. Visualization of networks
  4. Creating networks
  5. Understanding connectivity
  6. Important parts of networks
  7. Shortest paths
  8. Networks from text
  9. Crawling the web
  10. Clustering in networks
  11. Acyclic networks
  12. Postscript

Private

Introduction

  1. networks
  2. types of networks and their description
  3. additional data structures (vectors, partitions, permutations, hierarchies, …)

Data

  1. key/label → number
  2. text -> network
  3. transformations
  4. random networks
  5. operations Kn, Pn, Cn, Sn, Knm; union, difference, match
  6. solving puzzles, shortest paths (one, all)
  7. neigbors networks: multivariate → network
  8. BiBTeX → networks synonims homonims
  9. crawling the WWW, IMDb
  10. genealogies (GED)
  11. molecules
  12. two-mode

Structure

  1. degrees, fitting
  2. connectivity, shortest paths
  3. acyclic networks, condensation
  4. visualization

Important parts

  1. measures (centralities and weights), cores, SPC, probability flow
  2. cuts
  3. islands (SPC, main path, CPM)
  4. skeltons (trees, k-neighbors, pathfinder)

Multiplication and derived networks

  1. coappearance,
  2. combinations
  3. subnetworks
  4. levels

Volume 2

  1. statistics, triads
  2. optimization, coloring
  3. pattern searching (motifs),
  4. Clustering and blockmodeling
  5. diffusion
  6. inductive definitions
  7. Monte Carlo
  8. simulations
  9. bigdata
  10. spatial networks

Volume 3

  1. temporal networks