Network embedding : theories, methods, and applications / Cheng Yang, Zhiyuan Liu, Cunchao Tu, Chuan Shi, Maosong Sun.Material type: TextSeries: Synthesis lectures on artificial intelligence and machine learning ; #48. | Synthesis digital library of engineering and computer sciencePublisher: San Rafael, California (1537 Fourth Street, 1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool Publishers, Description: 1 PDF (xxi, 230 pages) : illustrations (some color)Content type: text Media type: electronic Carrier type: online resourceISBN: 9781636390451Subject(s): Machine learning | Neural networks (Computer science) | Vector spaces | network embedding | network representation learning | node embedding | graph neural network | graph convolutional network | social network | network analysis | deep learningGenre/Form: Electronic books.Additional physical formats: Print version:: No titleDDC classification: 006.3/1 LOC classification: Q325.5 | .Y366 2021ebOnline resources: Abstract with links to full text | Abstract with links to resource Also available in print.
|Item type||Current library||Call number||Status||Date due||Barcode|
|Ebooks||Indian Institute of Technology Delhi - Central Library||Available|
Mode of access: World Wide Web.
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Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 193-217).
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
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
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
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
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
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 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
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
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
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
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
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.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF title page (viewed on April 2, 2021).