Screen LibrarySystems for Big Graph Analytics /
There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investme...
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| Main Authors: | |
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| Corporate Authors: | |
| Group Author: | ; |
| Published: |
Springer International Publishing : Imprint: Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Series: |
SpringerBriefs in Computer Science,
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| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-319-58217-7 |
| Summary: |
There has been a surging interest in developing systems for analyzing big graphs generated by real applications, such as online social networks and knowledge graphs. This book aims to help readers get familiar with the computation models of various graph processing systems with minimal time investment. This book is organized into three parts, addressing three popular computation models for big graph analytics: think-like-a-vertex, think-likea- graph, and think-like-a-matrix. While vertex-centric systems have gained great popularity, the latter two models are currently being actively studied |
| Carrier Form: | 1 online resource(VI,92pages): illustrations. |
| ISBN: | 9783319582177 |
| Index Number: | QA75 |
| CLC: | TP3-05 |
| Contents: | 1 Introduction -- 2 Pregel-Like Systems -- 3 Hands-On Experiences -- 4 Shared Memory Abstraction -- 5 Block-Centric Computation -- 6 Subgraph-Centric Graph Mining -- 7 Matrix-Based Graph Systems -- 8 Conclusions. |