![]() ![]() ![]() We show that structured information networks are informative, and link analysis on such networks is powerful at uncovering critical knowledge hidden in large networks. We explore the power of mining such semi-structured heterogeneous information networks by introducing several interesting new data mining methodologies, including integrated ranking and clustering, classification, data integration, trust analysis, role discovery and prediction. 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. 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. Abstract – Objects in the real world are interconnected, often forming complex heterogeneous but semi-structured information networks. ![]()
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