Community search over big graphs /
By: Huang, Xin (Computer scientist) [author.].
Contributor(s): Lakshmanan, Laks V. S [author.] | Xu, Jianliang [author.] | Jagadish, H. V [author.].
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on data management: #67.Publisher: [San Rafael, California] : Morgan & Claypool, [2019]Description: 1 PDF (xvii, 188 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681735962.Subject(s): Communities -- Research -- Data processing | Social media -- Research -- Data processing | Big data | Graphic methods | big data | big graphs | social networks | community detection | community search | dense subgraph | cohesive subgraph | attributed community | geo-spatial community | social circle | k-core | k-trussDDC classification: 001.4/2 Online resources: Abstract with links to full text | Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
|
PK Kelkar Library, IIT Kanpur | Available | EBKE927 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 169-185).
8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.
1. Introduction -- 1.1. Graphs and communities -- 1.2. Community search -- 1.3. Prerequisite and target reader -- 1.4. Outline of the book
2. Cohesive subgraphs -- 2.1. Community search and cohesive subgraphs -- 2.2. Notations and notions -- 2.3. Classical dense subgraphs -- 2.4. K-core and k-truss -- 2.5. More dense subgraphs -- 2.6. Summary
3. Cohesive community search -- 3.1. Quasi-clique community models -- 3.2. Core-based community models -- 3.3. Truss-based community models -- 3.4. Query-biased densest community model -- 3.5. Summary
4. Attributed community search -- 4.1. Introduction -- 4.2. k-core-based attribute community model -- 4.3. k-truss-based attribute community model -- 4.4. Summary
5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles
6. Geo-social group search -- 6.1. Geo-social group search -- 6.2. Proximity-based geo-social group search -- 6.3. Geo-social k-cover group search -- 6.4. Geo-social group search based on minimum covering circle
7. Datasets and tools -- 7.1. Real-world datasets -- 7.2. Query generation and evaluation -- 7.3. Software and demo systems -- 7.4. Suggestions on dense subgraph selection for community models
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF title page (viewed on June 26, 2019).
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