Mining Heterogeneous Information Networks

Mining Heterogeneous Information Networks by Jiawei Han - Machine Learning Summer School at Purdue, 2011. Multiple typed objects in the real world are interconnected, forming complex heterogeneous information networks. Different from some studies on social network analysis where friendship networks or web page networks form homogeneous information networks, heterogeneous information network reflect complex and structured relationships among multiple typed objects. For example, in a university network, objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing rich information.

We explore methodologies on mining such structured information networks and introduce several interesting new mining methodologies, including integrated ranking and clustering, classification, role discovery, data integration, data validation, and similarity search. We show that structured information networks are informative, and link analysis on such networks becomes powerful at uncovering critical knowledge hidden in large networks. The tutorial also presents a few promising research directions on mining heterogeneous information networks.

Lecture 1 - Mining Heterogeneous Information Networks (Part 1)
Lecture 2 - Mining Heterogeneous Information Networks (Part 2)

Machine Learning Summer School at Purdue, 2011
A Machine Learning Approach for Complex Information Retrieval Applications
A Short Course on Reinforcement Learning
Classic and Modern Data Clustering
Divide and Recombine for the Analysis of Big Data
Graphical Models for the Internet
Introduction to Machine Learning
Large-Scale Machine Learning and Stochastic Algorithms
Machine Learning for a Rainy Day
Machine Learning for Discovery in Legal Cases
Machine Learning for Statistical Genetics
Mining Heterogeneous Information Networks
Modeling Complex Social Networks
Optimization for Machine Learning
Privacy Issues with Machine Learning: Fears, Facts, and Opportunities
Survey of Boosting from an Optimization Perspective
The MASH Project