Tutorials

To quickly demonstrate how the GraphLab framework can be used to solve a real-world Machine Learning problem we provide a short tutorial of how to implement PageRank in GraphLab.

PageRank Example

To provide a detailed overview of how all of the pieces of GraphLab can be put together to build an interesting algorithm we provide a lengthy tutorial of a toy "Coin Flipping" problem.

Detailed Example

To provide an example on how to implement linear algebra iterative algorithms for solving systems of linear equations, the following examples shows how to implement the Jacobi method.

Jacobi Example

GraphLab Libraries

Below are current application libraries on top of GraphLab

GraphLab linear solvers library

This application solves a linear system of equations using iterative solvers: Jacobi, Gaussian Belief Propagation (GaBP), Conjugate gradient, inverse of sparse symmetric matrix via GaBP, Shotgun LASSO solver and Shotgun sparse logistic regression solver.

GraphLab collaborative filtering library

This library implements multiple algorithms for factorizing a 3D tensor or a 2D matrix into lower rank matrices. Implemented algorithms are: PMF (probabalistic matrix factorization), BPTF (Bayesian probablistic tensor factorization), ALS (alternating least squares), WALS (weighted alternating least squares), SGD (stochastic gradient descent), SVD (Lanczos algorithm), NMF (non-negative matrix factorization) and Koren's SVD++ algorithm.

GraphLab clustering library

This library implements multiple clustering methods like K-Means, Fuzzy-Kmeans, Kmeans++, LDA (Latent Dirichlet Allocation), K-Core decomposition.

Non-parametric belief propagation

Code for running Non-parametric belief propagation algorithm, for computing inference using Gaussian mixture model.

User applications

• Label propagation algorithm by Akshay Bhat, Cornell, project page.
  GraphLab code is found here.
• Parallel Gibbs sampling by Joey Gonzalez, CMU, project page.
  GraphLab code is found here