找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Cohesive Subgraph Computation over Large Sparse Graphs; Algorithms, Data Str Lijun Chang,Lu Qin Book 2018 Springer Nature Switzerland AG 20

[復制鏈接]
查看: 6571|回復: 36
樓主
發(fā)表于 2025-3-21 16:16:47 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cohesive Subgraph Computation over Large Sparse Graphs
副標題Algorithms, Data Str
編輯Lijun Chang,Lu Qin
視頻videohttp://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
叢書名稱Springer Series in the Data Sciences
圖書封面Titlebook: Cohesive Subgraph Computation over Large Sparse Graphs; Algorithms, Data Str Lijun Chang,Lu Qin Book 2018 Springer Nature Switzerland AG 20
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
關鍵詞Cohesive Subgraph Computation; K-Core; Densest Subgraph; K-Edge Connected Component; Maximum Clique; data
版次1
doihttps://doi.org/10.1007/978-3-030-03599-0
isbn_ebook978-3-030-03599-0Series ISSN 2365-5674 Series E-ISSN 2365-5682
issn_series 2365-5674
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

書目名稱Cohesive Subgraph Computation over Large Sparse Graphs影響因子(影響力)




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs影響因子(影響力)學科排名




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs網絡公開度




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs網絡公開度學科排名




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs被引頻次




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs被引頻次學科排名




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs年度引用




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs年度引用學科排名




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs讀者反饋




書目名稱Cohesive Subgraph Computation over Large Sparse Graphs讀者反饋學科排名




單選投票, 共有 1 人參與投票
 

0票 0.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

1票 100.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用戶組沒有投票權限
沙發(fā)
發(fā)表于 2025-3-21 22:28:10 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:58:50 | 只看該作者
2365-5674 chniques for efficient cohesive subgraph computation.Source 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 ric
地板
發(fā)表于 2025-3-22 05:44:24 | 只看該作者
Book 2018information 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 ex
5#
發(fā)表于 2025-3-22 11:58:59 | 只看該作者
6#
發(fā)表于 2025-3-22 13:09:07 | 只看該作者
7#
發(fā)表于 2025-3-22 20:13:46 | 只看該作者
8#
發(fā)表于 2025-3-23 00:02:18 | 只看該作者
Introduction, are accumulated with data entities involving complex relationships. These data are usually modelled as . in view of the simple yet strong expressive power of graph model; that is, entities are represented by vertices and relationships are represented by edges.
9#
發(fā)表于 2025-3-23 03:11:32 | 只看該作者
Average Degree-Based Densest Subgraph Computation,terature. In Section?., we give preliminaries of densest subgraphs. Approximation algorithms and exact algorithms for computing the densest subgraph of a large input graph will be discussed in Section?. and in Section?., respectively.
10#
發(fā)表于 2025-3-23 07:17:11 | 只看該作者
 關于派博傳思  派博傳思旗下網站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網 吾愛論文網 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經驗總結 SCIENCEGARD IMPACTFACTOR 派博系數 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網安備110108008328) GMT+8, 2026-1-25 08:44
Copyright © 2001-2015 派博傳思   京公網安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
绥棱县| 海门市| 曲靖市| 西城区| 锦州市| 漳平市| 大同市| 汉源县| 阜城县| 南宁市| 陈巴尔虎旗| 堆龙德庆县| 娄底市| 汉源县| 南木林县| 皮山县| 湄潭县| 乾安县| 陵川县| 龙江县| 乌拉特前旗| 砚山县| 潜江市| 济阳县| 五莲县| 东乌珠穆沁旗| 武夷山市| 贵溪市| 绥芬河市| 伊宁市| 道孚县| 诸城市| 乐亭县| 纳雍县| 武冈市| 修水县| 崇文区| 芜湖县| 丁青县| 绥宁县| 丹棱县|