Mining Heterogeneous Information Networks: Principles and Methodologies

摘要: Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these interactions between into multiple types, such networks become semi-structured heterogeneous information Most real-world applications that handle big data, including social media networks, scientific, engineering, or medical systems, online e-commerce most database can be structured Therefore, effective analysis of large-scale poses an interesting but critical challenge. In this book, we investigate the principles methodologies mining Departing from many existing network models view as homogeneous graphs our model leverages rich semantics typed nodes links in a uncovers surprisingly knowledge network. This modeling leads to series new powerful for including: (1) rank-based clustering classification; (2) meta-path-based similarity search mining; (3) relation strength-aware mining, other potential developments. book introduces research frontier points out some promising directions. Table Contents: Introduction / Ranking-Based Clustering Classification Heterogeneous Information Networks Meta-Path-Based Similarity Search Relationship Prediction Relation Strength-Aware with Incomplete Attributes User-Guided via Meta-Path Selection Research Frontiers