Network embedding: theories, methods, and applications
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...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2021]
|
| Schriftenreihe: | Synthesis lectures on artificial intelligence and machine learning
#48 |
| Schlagworte: | |
| Online-Zugang: | UBR01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (xxi, 220 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781636390451 |
| DOI: | 10.2200/S01063ED1V01Y202012AIM048 |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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 | ||
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| 700 | 1 | |a Tu, Cunchao |e Verfasser |4 aut | |
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| 700 | 1 | |a Sun, Maosong |e Verfasser |0 (DE-588)1060349175 |4 aut | |
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Datensatz im Suchindex
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| author | Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong |
| author_GND | (DE-588)1232732672 (DE-588)1060349175 |
| author_facet | Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong |
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| author_sort | Yang, Cheng |
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| building | Verbundindex |
| bvnumber | BV047632852 |
| classification_rvk | ST 153 |
| collection | ZDB-105-MCB |
| contents | 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 |
| ctrlnum | (OCoLC)1289781145 (DE-599)BVBBV047632852 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| doi_str_mv | 10.2200/S01063ED1V01Y202012AIM048 |
| format | Electronic eBook |
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| id | DE-604.BV047632852 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:45:55Z |
| indexdate | 2024-07-10T09:17:45Z |
| institution | BVB |
| isbn | 9781636390451 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033017206 |
| oclc_num | 1289781145 |
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| owner_facet | DE-355 DE-BY-UBR |
| physical | 1 Online-Ressource (xxi, 220 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-105-MCB ZDB-105-MCB UBR Pick&Choose 2021 |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Morgan & Claypool Publishers |
| record_format | marc |
| series | Synthesis lectures on artificial intelligence and machine learning |
| series2 | Synthesis lectures on artificial intelligence and machine learning |
| spelling | Yang, Cheng Verfasser aut Network embedding theories, methods, and applications Cheng Yang (Beijing University of Posts and Telecommunications, China), Zhiyuan Liu (Tsinghua University, China) Cunchao Tu (Tsinghua University, China), Chuan Shi (Beijing University of Posts and Telecommunications, China), Maosong Sun (Tsinghua University, China) [San Rafael, California] Morgan & Claypool Publishers [2021] 1 Online-Ressource (xxi, 220 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on artificial intelligence and machine learning #48 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 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 Machine learning Neural networks (Computer science) Vector spaces Machine learning fast Neural networks (Computer science) fast Vector spaces fast Liu, Zhiyuan Verfasser aut Tu, Cunchao Verfasser aut Shi, Chuan Verfasser (DE-588)1232732672 aut Sun, Maosong Verfasser (DE-588)1060349175 aut Erscheint auch als Druck-Ausgabe, Paperback 978-1-63639-044-4 Erscheint auch als Druck-Ausgabe, Hardcover 978-1-63639-046-8 Synthesis lectures on artificial intelligence and machine learning #48 (DE-604)BV043983076 48 https://doi.org/10.2200/S01063ED1V01Y202012AIM048 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong Network embedding theories, methods, and applications Synthesis lectures on artificial intelligence and machine learning 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 Machine learning Neural networks (Computer science) Vector spaces Machine learning fast Neural networks (Computer science) fast Vector spaces fast |
| title | Network embedding theories, methods, and applications |
| title_auth | Network embedding theories, methods, and applications |
| title_exact_search | Network embedding theories, methods, and applications |
| title_exact_search_txtP | Network embedding theories, methods, and applications |
| title_full | Network embedding theories, methods, and applications Cheng Yang (Beijing University of Posts and Telecommunications, China), Zhiyuan Liu (Tsinghua University, China) Cunchao Tu (Tsinghua University, China), Chuan Shi (Beijing University of Posts and Telecommunications, China), Maosong Sun (Tsinghua University, China) |
| title_fullStr | Network embedding theories, methods, and applications Cheng Yang (Beijing University of Posts and Telecommunications, China), Zhiyuan Liu (Tsinghua University, China) Cunchao Tu (Tsinghua University, China), Chuan Shi (Beijing University of Posts and Telecommunications, China), Maosong Sun (Tsinghua University, China) |
| title_full_unstemmed | Network embedding theories, methods, and applications Cheng Yang (Beijing University of Posts and Telecommunications, China), Zhiyuan Liu (Tsinghua University, China) Cunchao Tu (Tsinghua University, China), Chuan Shi (Beijing University of Posts and Telecommunications, China), Maosong Sun (Tsinghua University, China) |
| title_short | Network embedding |
| title_sort | network embedding theories methods and applications |
| title_sub | theories, methods, and applications |
| topic | Machine learning Neural networks (Computer science) Vector spaces Machine learning fast Neural networks (Computer science) fast Vector spaces fast |
| topic_facet | Machine learning Neural networks (Computer science) Vector spaces |
| url | https://doi.org/10.2200/S01063ED1V01Y202012AIM048 |
| volume_link | (DE-604)BV043983076 |
| work_keys_str_mv | AT yangcheng networkembeddingtheoriesmethodsandapplications AT liuzhiyuan networkembeddingtheoriesmethodsandapplications AT tucunchao networkembeddingtheoriesmethodsandapplications AT shichuan networkembeddingtheoriesmethodsandapplications AT sunmaosong networkembeddingtheoriesmethodsandapplications |