## Tutorial at KDD 2018

## Title: Graph and Tensor Mining for Fun and Profit

### Instructors:

Xin Luna Dong (Amazon),
Christos Faloutsos (Amazon and CMU),
Andrey Kan (Amazon),
Subhabrata Mukherjee (Amazon), and
Jun Ma (Amazon)
### Short Description

Given a large graph, which is the most important node?
Can we plot and visualize the nodes in a low-dimensional space?
Given a * heterogeneous* graph (where edges have attributes),
like a knowledge graph, are there regularities? anomalies?

These
questions and several related ones, have attracted huge interest,
resulting in milestone algorithms like PageRank, HITS, recommendation systems,
Belief Propagation,
`word2vec', and several more.
This tutorial surveys all these algorithms,
focusing on the * intuition* behind them
(as opposed to the mathematical analysis); it highlights
their strengths, similarities, and illustrates
their applicability to real-world problems.
### Longer Description

In pdf