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

FOILS - In single zip file

In single zip file (Caution: large - 26Mb)

FOILS - per individual part

Introduction
Part 1: Graphs

Part 2: Tensors

Conclusions

Last updated: Aug. 13, 2018, by Christos Faloutsos