標題: Titlebook: Cohesive Subgraph Computation over Large Sparse Graphs; Algorithms, Data Str Lijun Chang,Lu Qin Book 2018 Springer Nature Switzerland AG 20 [打印本頁] 作者: Amalgam 時間: 2025-3-21 16:16
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(xué)科排名
書目名稱Cohesive Subgraph Computation over Large Sparse Graphs網(wǎng)絡(luò)公開度
書目名稱Cohesive Subgraph Computation over Large Sparse Graphs網(wǎng)絡(luò)公開度學(xué)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(xué)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(xué)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(xué)科排名
作者: 代理人 時間: 2025-3-21 22:28 作者: Outspoken 時間: 2025-3-22 01:58
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作者: Pelago 時間: 2025-3-22 05:44
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作者: hypertension 時間: 2025-3-22 11:58 作者: 一小塊 時間: 2025-3-22 13:09 作者: 一小塊 時間: 2025-3-22 20:13 作者: 不自然 時間: 2025-3-23 00:02
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.作者: 擁護 時間: 2025-3-23 03:11
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.作者: Intruder 時間: 2025-3-23 07:17 作者: opalescence 時間: 2025-3-23 09:53
The Adaptive Radiation of Proprioceptorsterature. 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.作者: mutineer 時間: 2025-3-23 14:33 作者: 免除責(zé)任 時間: 2025-3-23 18:54 作者: SMART 時間: 2025-3-23 22:26 作者: 延期 時間: 2025-3-24 04:09 作者: tenosynovitis 時間: 2025-3-24 08:04 作者: 改革運動 時間: 2025-3-24 11:30
Taxes in Unicells, Especially ProtozoaIn this chapter, we present linear heap data structures that will be useful in the remainder of the book for designing algorithms to efficiently process large sparse graphs.作者: 哺乳動物 時間: 2025-3-24 17:37 作者: 神刊 時間: 2025-3-24 22:42
Ecosensory Functions in Lower InvertebratesHigher-order structures, also known as motifs or graphlets, have been recently used to successfully locate dense regions that cannot be detected otherwise by edge-centric methods?[., ., .].作者: 遍及 時間: 2025-3-25 00:10
Adaptive Radiation of MechanoreceptionIn this chapter, we study edge connectivity-based graph decomposition; that is, each subgraph satisfies a certain edge connectivity requirement.作者: 佛刊 時間: 2025-3-25 04:31 作者: 不成比例 時間: 2025-3-25 10:57
Minimum Degree-Based Core Decomposition,In this chapter, we discuss efficient techniques for computing the minimum degree-based graph decomposition (aka, .). Preliminaries are given in Section?.. A linear-time algorithm is presented in Section?., while .-based local algorithms that can be naturally made parallel are presented in Section?..作者: 細微的差異 時間: 2025-3-25 15:29
Higher-Order Structure-Based Graph Decomposition,Higher-order structures, also known as motifs or graphlets, have been recently used to successfully locate dense regions that cannot be detected otherwise by edge-centric methods?[., ., .].作者: 夾克怕包裹 時間: 2025-3-25 16:36
Edge Connectivity-Based Graph Decomposition,In this chapter, we study edge connectivity-based graph decomposition; that is, each subgraph satisfies a certain edge connectivity requirement.作者: commonsense 時間: 2025-3-25 23:20 作者: 傻瓜 時間: 2025-3-26 00:32
Springer Series in the Data Scienceshttp://image.papertrans.cn/c/image/229241.jpg作者: 流出 時間: 2025-3-26 08:12
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