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