This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.
1. Introduction1.1 Basic concepts and definitions1.2 Comparisons with related concepts1.3 Example Datasets of HIN1.4 Why Heterogeneous Information Network Analysis1.5 Organization of the book2. Summarization of the developments2.1 Similarity search2.2 Clustering2.3 Classification2.4 Link Prediction2.5 Ranking2.6 Recommendation2.7 Information fusion2.8 Other applications2.9 Application systems3. Uniform relevance measure of heterogeneous objects4. Path based Ranking5. Ranking based Clustering6. Recommendation with heterogeneous information7. Information fusion with heterogeneous network8. Prototype system9. Future research directions10. Conclusion
