Network embedding : theories methods and applications 4. Revisiting attributed network embedding : a GCN based perspective 4.1. GCN based network embedding 4.2. Method : adaptive graph encoder 4.3. Empirical analysis 4.4. Further reading Part III. Network embedding with different characteristics. 7. Network embedding for community structured graphs 7.1. Overview 7.2. Method : community enhanced NRL 7.3. Empirical analysis 7.4. Further reading Part I. Introduction to network embedding. 1. The basics of network embedding 1.1. Background 1.2. The rising of network embedding 1.3. The evaluation of network embedding Part II. Network embedding with additional information. 3. Network embedding for graphs with node attributes 3.1. Overview 3.2. Method : text associated DeepWalk 3.3. Empirical analysis 3.4. Further reading 9. Network embedding for heterogeneous graphs 9.1. Overview 9.2. Method : relation structure aware HIN embedding 9.3. Empirical analysis 9.4. Further reading 5. Network embedding for graphs with node contents 5.1. Overview 5.2. Method : context aware network embedding 5.3. Empirical analysis 5.4. Further reading 2. Network embedding for general graphs 2.1. Representative methods 2.2. Theory : a unified network embedding framework 2.3. Method : network embedding update (NEU) 2.4. Empirical analysis 2.5. Further reading 8. Network embedding for large scale graphs 8.1. Overview 8.2. Method : COmpresSIve network embedding (COSINE) 8.3. Empirical analysis 8.4. Further reading Part V. Outlook for network embedding. 13. Future directions of network embedding 13.1. Network embedding based on advanced techniques 13.2. Network embedding in more fine grained scenarios 13.3. Network embedding with better interpretability 13.4. Network embedding for applications. 11. Network embedding for recommendation systems on LBSNs 11.1. Overview 11.2. Method : joint network and trajectory model (JNTM) 11.3. Empirical analysis 11.4. Further reading 12. Network embedding for information diffusion prediction 12.1. Overview 12.2. Method : neural diffusion model (NDM) 12.3. Empirical analysis 12.4. Further reading Many machine learning algorithms require real valued feature vectors of data instances as inputs. By projecting data into vector spaces representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency NE has been widely applied in many network applications such as node classification and link prediction. This book provides a comprehensive introduction to the basic concepts models and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then it introduces the development of NE techniques by presenting several representative methods on general graphs as well as a unified NE framework based on matrix factorization. Afterward it presents the variants of NE with additional information: NE for graphs with node attributes contents labels; and the variants with different characteristics: NE for community structured large scale heterogeneous graphs. Further the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally the book concludes the methods and applications and looks forward to the future directions 6. Network embedding for graphs with node labels 6.1. Overview 6.2. Method : max margin DeepWalk 6.3. Empirical analysis 6.4. Further reading Part IV. Network embedding applications. 10. Network embedding for social relation extraction 10.1. Overview 10.2. Method : TransNet 10.3. Empirical analysis 10.4. Further reading