GraphLab: Distributed Graph-Parallel API  2.2
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GraphLab Toolkits

To enable users to use GraphLab out-of-the-box and to demonstrate the power of the GraphLab API we have implemented a collection of applications to address a wide range of standard tasks in large-scale graph computation.

We have implemented the following toolkits

  • Topic Modeling contains applications like LDA which can be used to cluster documents and extract topical representations.
  • graph_algorithms contains algorithms mostly from the Social Network Analysis Handbook algorithms set.
  • Graph Analytics contains application like pagerank and triangle counting which can be applied to general graphs to estimate community structure.
  • Clustering contains standard data clustering tools such as Kmeans
  • Collaborative Filtering contains a collection of applications used to make predictions about users interests and factorize large matrices.
  • Graphical Models contains tools for making joint predictions about collections of related random variables.
  • Linear iterative solver contains solvers for linear systems of equations - currently the Jacobi algorithm is implemented
  • Computer Vision contains a collection of tools for reasoning about images.