Heterogeneous information network analysis and applications / Chuan Shi, Philip S. Yu.

Preface; Contents; 1 Introduction; 1.1 Basic Concepts and Definitions; 1.2 Comparisons with Related Concepts; 1.3 Example Datasets of Heterogeneous Information Networks; 1.4 Why Heterogeneous Information Network Analysis; References; 2 Survey of Current Developments ; 2.1 Similarity Search; 2.2 Clustering; 2.3 Classification; 2.4 Ranking; 2.5 Link Prediction; 2.6 Recommendation; 2.7 Information Fusion; 2.8 Other Applications; References; 3 Relevance Measure of Heterogeneous Objects; 3.1 HeteSim: A Uniform and Symmetric Relevance Measure; 3.1.1 Overview; 3.1.2 The HeteSim Measure

3.1.3 Experiments3.1.4 Quick Computation Strategies and Experiments; 3.2 Extension of HeteSim; 3.2.1 Overview; 3.2.2 AvgSim: A New Relevance Measure; 3.2.3 Parallelization of AvgSim; 3.2.4 Experiments; 3.3 Conclusion; References; 4 Path-Based Ranking and Clustering; 4.1 Meta Path-Based Ranking; 4.1.1 Overview; 4.1.2 The HRank Method; 4.1.3 Experiments; 4.2 Ranking-Based Clustering; 4.2.1 Overview; 4.2.2 Problem Formulation; 4.2.3 The HeProjI Algorithm; 4.2.4 Experiments; 4.3 Conclusions; References; 5 Recommendation with Heterogeneous Information; 5.1 Recommendation Based on Semantic Path

5.1.1 Overview5.1.2 Heterogeneous Network Framework for Recommendation; 5.1.3 The SemRec Solution; 5.1.4 Experiments; 5.2 Recommendation Based on Matrix Factorization; 5.2.1 Overview; 5.2.2 The SimMF Method; 5.2.3 Experiments; 5.3 Social Recommendation with Heterogeneous Information; 5.3.1 Overview; 5.3.2 The DSR Method; 5.3.3 Experiments; 5.4 Conclusions; References; 6 Fusion Learning on Heterogeneous Social Networks; 6.1 Network Alignment; 6.1.1 Overview; 6.1.2 Terminology Definition and Social Meta Path; 6.1.3 Cross-Network Network Alignment; 6.1.4 Experiments

6.2 Link Transfer Across Aligned Networks6.2.1 Overview; 6.2.2 Cross-Network Link Prediction; 6.2.3 Experiments; 6.3 Synergistic Network Community Detection; 6.3.1 Overview; 6.3.2 Cross-Network Community Detection; 6.3.3 Experiments; 6.4 Conclusions; References; 7 Schema-Rich Heterogeneous Network Mining; 7.1 Link Prediction in Schema-Rich Heterogeneous Network; 7.1.1 Overview; 7.1.2 The LiPaP Method; 7.1.3 Experiments; 7.2 Entity Set Expansion with Meta Path in Knowledge Graph; 7.2.1 Overview; 7.2.2 The MP_ESE Method; 7.2.3 Experiments; 7.3 Conclusions; References

8 Prototype System Based on Heterogeneous Network8.1 Semantic Recommender System; 8.1.1 Overview; 8.1.2 System Architecture; 8.1.3 System Implementation; 8.1.4 System Demonstration; 8.2 Explainable Recommender System; 8.2.1 Overview; 8.2.2 Heterogeneous Network-Based Recommendation; 8.2.3 System Framework; 8.2.4 System Demonstration; 8.3 Other Prototype Systems on Heterogeneous Network; 8.4 Conclusions; References; 9 Future Research Directions; 9.1 More Complex Network Construction; 9.2 More Powerful Mining Methods; 9.2.1 Network Structure; 9.2.2 Semantic Mining; 9.3 Bigger Networked Data