Graph Computing Systems and Partitioning Techniques: A Survey

Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have beco...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.1-1
Main Authors: Ayall, Tewodros Alemu, Liu, Huawen, Zhou, Changjun, Seid, Abegaz Mohammed, Gereme, Fantahun Bogale, Abishu, Hayla Nahom, Yacob, Yasin Habtamu
Format: Article
Language:English
Subjects:
Apexes
Classification algorithms
Computation
Computational modeling
Data models
Distributed computing
Domains
Global Positioning System
graph algorithm
graph computing systems
graph databases
graph partitioning
graph processing systems
Graph theory
Graphs
Heuristic algorithms
Large-scale graphs
Machine learning
Network analysis
Partitioning
Partitioning algorithms
Recommender systems
Social networking (online)
Social networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have recently proposed graph partitioning and computing systems to efficiently process and analyze large-scale graphs. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss future challenges and research directions in graph partitioning and computing systems.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3219422