Here is a list of all related documentation pages:

[detail level 12]

FAQ
GraphLab Tutorial
1: Creating a GraphLab project
2: Hello World in GraphLab
3: Defining a Graph
4: Loading Graph Data
5: Writing the PageRank Vertex Program
6: Runtime Scheduling
7: Saving Results
8: Conclusion
Graph File Formats
GraphLab RPC
Serialization
Linear iterative solver	GraphLab linear solver library is used for solving the linear system Ax = b
GraphLab Toolkits
Topic Modeling	The topic modeling toolkit contains a collection of applications targeted at clustering documents and extracting topical representations. The resulting topical representation can be used as a feature space in information retrieval tasks and to group topically related words and documents
Graph Analytics
Clustering
Collaborative Filtering	The collaborative filtering toolkit contains tools for computing a linear model of the data, and predicting missing values based on this linear model. This is useful when computing recommendations for users
Graphical Models	The Graphical Models toolkit contains a collection of applications for reasoning about structured noisy data. Graphical models provide a compact interpretable representation of complex statistical phenomena by encoding random variables as vertices in a graph and relationships between those variables as edges. Given a graphical model representation, we can then apply Bayes rule to quantitatively infer properties of some variables given observations about others. Graphical models also provide the unique ability to quantify uncertainty in our prediction
Computer Vision	GraphLab Computer Vision Toolkit aims to provide fully distributed wrappers to algorithms in OpenCV, an open-source library aimed at real-time computer vision. Eventually, GraphLab Computer Vision Toolkit will become it’s own spin-off project called CloudCV, a system that will provide access to state-of-the-art computer vision algorithms on the cloud