Title Statements
- Full title :Community search over big graphs / Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
Summary
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.
Read More ↓Edition Statements
- Publication Date:2019
Publication Statements
- Publisher(s):Morgan & Claypool Publishers
- Place of Publication:San Rafael, California
- Language:eng
- Publication Date:2019
The Physical Object
- Description:1 online resource (xvii, 188 pages :) : illustrations.
Edition Identifiers
- oclc:on1138952251
- LC Classification:QA76.9.B45
- Dewey Decimal:001.4/2
Table of Contents
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…
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