Recent Advances in Efficient Dynamic Graph Processing

Graph as one of the most fundamental and representative data structures has found a wide spectrum of emerging application domains such as social media, financial transactions, biology science, and road networks. Recently, with the proliferation of graph applications, graph processing has attracted m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Applied sciences Jg. 15; H. 11; S. 6003
Hauptverfasser: Chen, Zi, Liang, Keke, Yuan, Long, Zhang, Wenjie, Yang, Zhengyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2025
Schlagworte:
Advertising
Algorithms
dynamic graph algorithms
Efficiency
fully graph update
graph analysis
Graph coloring
Graph representations
Proteins
Social media
Social network analysis
Social networks
Surveys
system
United States
ISSN:2076-3417, 2076-3417
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Graph as one of the most fundamental and representative data structures has found a wide spectrum of emerging application domains such as social media, financial transactions, biology science, and road networks. Recently, with the proliferation of graph applications, graph processing has attracted much attention in both industry and academia. Among them, most existing works focus on the static graphs in which the vertices and edges are immutable. However, in the real world, graphs are constantly and dynamically changing, bringing tricky challenges to process such dynamic graphs. This paper surveys the recent advances in dynamic graph processing, including centrality, graph coloring, cohesive subgraph, path traversal, and graph separation. We summarize the computational complexity models for dynamic algorithm analysis, theoretically compare the efficiency of algorithms among different research topics. Moreover, we also explore the research opportunities for the future.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app15116003