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