Community search over big graphs:
Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple,...
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool Publishers
[2019]
|
| Schriftenreihe: | Synthesis lectures on data management
#61 |
| Schlagwörter: | |
| Links: | https://doi.org/10.2200/S00928ED1V01Y201906DTM061 |
| Zusammenfassung: | Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area |
| Beschreibung: | Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on June 26, 2019) |
| Umfang: | 1 Online-Resource (xvii, 188 Seiten) Illustrationen |
| ISBN: | 9781681735962 |
| DOI: | 10.2200/S00928ED1V01Y201906DTM061 |
Internformat
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| 520 | |a Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area | ||
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| author | Huang, Xin Lakshmanan, Laks V. S. Xu, Jianliang |
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| discipline | Allgemeines Informatik |
| doi_str_mv | 10.2200/S00928ED1V01Y201906DTM061 |
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| id | DE-604.BV046427712 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:51:57Z |
| institution | BVB |
| isbn | 9781681735962 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031840014 |
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| physical | 1 Online-Resource (xvii, 188 Seiten) Illustrationen |
| psigel | ZDB-105-MCS ZDB-105-MCDM |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Morgan & Claypool Publishers |
| record_format | marc |
| series | Synthesis lectures on data management |
| series2 | Synthesis lectures on data management |
| spelling | Huang, Xin Verfasser aut Community search over big graphs Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu [San Rafael, California] Morgan & Claypool Publishers [2019] © 2019 1 Online-Resource (xvii, 188 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on data management #61 Part of: Synthesis digital library of engineering and computer science Title from PDF title page (viewed on June 26, 2019) Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area Communities Research Data processing Social media Research Data processing Big data Graphic methods Graphpartitionierung (DE-588)105875100X gnd rswk-swf Teilgraph (DE-588)4280772-4 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf Graph (DE-588)4021842-9 gnd rswk-swf Graph (DE-588)4021842-9 s Big Data (DE-588)4802620-7 s Teilgraph (DE-588)4280772-4 s Graphpartitionierung (DE-588)105875100X s DE-604 Lakshmanan, Laks V. S. Verfasser (DE-588)1158362854 aut Xu, Jianliang Verfasser (DE-588)1011428687 aut Erscheint auch als Druck-Ausgabe, paperback 978-1-68173-595-5 Erscheint auch als Druck-Ausgabe, hardcover 978-1-68173-597-9 Synthesis lectures on data management #61 (DE-604)BV036731811 61 https://doi.org/10.2200/S00928ED1V01Y201906DTM061 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Huang, Xin Lakshmanan, Laks V. S. Xu, Jianliang Community search over big graphs Synthesis lectures on data management Communities Research Data processing Social media Research Data processing Big data Graphic methods Graphpartitionierung (DE-588)105875100X gnd Teilgraph (DE-588)4280772-4 gnd Big Data (DE-588)4802620-7 gnd Graph (DE-588)4021842-9 gnd |
| subject_GND | (DE-588)105875100X (DE-588)4280772-4 (DE-588)4802620-7 (DE-588)4021842-9 |
| title | Community search over big graphs |
| title_auth | Community search over big graphs |
| title_exact_search | Community search over big graphs |
| title_full | Community search over big graphs Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu |
| title_fullStr | Community search over big graphs Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu |
| title_full_unstemmed | Community search over big graphs Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu |
| title_short | Community search over big graphs |
| title_sort | community search over big graphs |
| topic | Communities Research Data processing Social media Research Data processing Big data Graphic methods Graphpartitionierung (DE-588)105875100X gnd Teilgraph (DE-588)4280772-4 gnd Big Data (DE-588)4802620-7 gnd Graph (DE-588)4021842-9 gnd |
| topic_facet | Communities Research Data processing Social media Research Data processing Big data Graphic methods Graphpartitionierung Teilgraph Big Data Graph |
| url | https://doi.org/10.2200/S00928ED1V01Y201906DTM061 |
| volume_link | (DE-604)BV036731811 |
| work_keys_str_mv | AT huangxin communitysearchoverbiggraphs AT lakshmananlaksvs communitysearchoverbiggraphs AT xujianliang communitysearchoverbiggraphs |