Good resources for graph analytics and learning

Here is a collection of videos and books on the topic of graph analytics and learning. I am collecting them for building a graph based domain specific language.

Here are a series of materials with a focus on videos on introducing graph computing for analytics, machine learning and other applications. I try to rank them in the order of importance and ease of understanding.

This is my attempt at understanding the graph applications, while trying to design a DSL (domain specific language) for graphs.

GraphLab (Strata NY 2013) GraphLab large scale machine learning on graphs. Carlos Guestrin, CEO

Despite its horrible performance, it is the first framework that puts graph computing in systems highlight.

Graph-Based User Behavior Modeling (CMU workshop at KDD 2015 on ACM)  (part 1)    (part 2)

Published on Oct 2, 2015

Authors: Alex Beutel, Leman Akoglu, Christos Faloutsos


How can we model users’ preferences? How do anomalies, fraud, and spam effect our models of normal users? How can we modify our models to catch fraudsters? In this tutorial we will answer these questions – connecting graph analysis tools for user behavior modeling to anomaly and fraud detection. In particular, we will focus on the application of subgraph analysis, label propagation, and latent factor models to static, evolving, and attributed graphs. For each of these techniques we will give a brief explanation of the algorithms and the intuition behind them. We will then give examples of recent research using the techniques to model, understand and predict normal behavior. With this intuition for how these methods are applied to graphs and user behavior, we will focus on state-of-the-art research showing how the outcomes of these methods are effected by fraud, and how they have been used to catch fraudsters.

Networks, Crowds, and Markets

This is an extremely good book for an introduction to the field. I used it as an undergraduate textbook on graph algorithms.

There is also a edX class that goes with it

Networks: an introduction (Mark Newman)

This is an incredible book giving clear description of the fundamental concepts, algorithms and ideas in graph computing. Covers graph theory and some spectral graph theory stuff. Mostly using the language of Linear Algebra.

Graph Algorithms in the Language of Linear Algebra

This book is very eye opening for me. It is interesting to see how linear algebra can be used for a lot of common graph applications with potentially very concise syntax. The authors are all experts in the field of graphs, sparse matrix operations and other relevant field.



There are good tutorials on how to use the Cipher language.


Probabilistic Graphical Models

There is also a coursera class by the same professor.

Machine Learning on Graphs

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