| 000 | 05525nam a2200841 i 4500 | ||
|---|---|---|---|
| 001 | 8792419 | ||
| 003 | IEEE | ||
| 005 | 20200413152933.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 190827s2019 caua ob 000 0 eng d | ||
| 020 |
_a9781681735962 _qelectronic |
||
| 020 |
_z9781681735979 _qhardcover |
||
| 020 |
_z9781681735955 _qpaperback |
||
| 024 | 7 |
_a10.2200/S00928ED1V01Y201906DTM061 _2doi |
|
| 035 | _a(CaBNVSL)thg00979391 | ||
| 035 | _a(OCoLC)1112420672 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aHM756 _b.G833 2019eb |
|
| 082 | 0 | 4 |
_a001.4/2 _223 |
| 100 | 1 |
_aHuang, Xin _c(Computer scientist), _eauthor. |
|
| 245 | 1 | 0 |
_aCommunity search over big graphs / _cXin Huang, Laks V.S. Lakshmanan, Jianliang Xu. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c[2019] |
|
| 300 |
_a1 PDF (xvii, 188 pages) : _billustrations (some color). |
||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aSynthesis lectures on data management, _x2153-5426 ; _v#61 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 169-185). | ||
| 505 | 8 | _a8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions. | |
| 505 | 0 | _a1. Introduction -- 1.1. Graphs and communities -- 1.2. Community search -- 1.3. Prerequisite and target reader -- 1.4. Outline of the book | |
| 505 | 8 | _a2. 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 | |
| 505 | 8 | _a3. 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 | |
| 505 | 8 | _a4. 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 | |
| 505 | 8 | _a5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles | |
| 505 | 8 | _a6. 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 | |
| 505 | 8 | _a7. 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 | |
| 506 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | _aCommunities 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. | ||
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on June 26, 2019). | ||
| 650 | 0 |
_aCommunities _xResearch _xData processing. |
|
| 650 | 0 |
_aSocial media _xResearch _xData processing. |
|
| 650 | 0 | _aBig data. | |
| 650 | 0 | _aGraphic methods. | |
| 653 | _abig data | ||
| 653 | _abig graphs | ||
| 653 | _asocial networks | ||
| 653 | _acommunity detection | ||
| 653 | _acommunity search | ||
| 653 | _adense subgraph | ||
| 653 | _acohesive subgraph | ||
| 653 | _aattributed community | ||
| 653 | _ageo-spatial community | ||
| 653 | _asocial circle | ||
| 653 | _ak-core | ||
| 653 | _ak-truss | ||
| 700 | 1 |
_aLakshmanan, Laks V. S., _d1959- _eauthor. |
|
| 700 | 1 |
_aXu, Jianliang, _d1976- _eauthor. |
|
| 700 | 1 |
_aJagadish, H. V., _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781681735955 _z9781681735979 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on data management ; _v#67. |
|
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttps://doi.org/10.2200/S00928ED1V01Y201906DTM061 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8792419 |
| 999 |
_c562427 _d562427 |
||