| 書(shū)目名稱 | Cohesive Subgraph Computation over Large Sparse Graphs |
| 副標(biāo)題 | Algorithms, Data Str |
| 編輯 | Lijun Chang,Lu Qin |
| 視頻video | http://file.papertrans.cn/230/229241/229241.mp4 |
| 概述 | Includes data structures that can be of general use for efficient graph processing.Considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation.Source |
| 叢書(shū)名稱 | Springer Series in the Data Sciences |
| 圖書(shū)封面 |  |
| 描述 | This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book..?.This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation.. |
| 出版日期 | Book 2018 |
| 關(guān)鍵詞 | Cohesive Subgraph Computation; K-Core; Densest Subgraph; K-Edge Connected Component; Maximum Clique; data |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-03599-0 |
| isbn_ebook | 978-3-030-03599-0Series ISSN 2365-5674 Series E-ISSN 2365-5682 |
| issn_series | 2365-5674 |
| copyright | Springer Nature Switzerland AG 2018 |
|Archiver|手機(jī)版|小黑屋|
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