Recent Advances in Efficient Dynamic Graph Processing
Zi Chen, Keke Liang, Long Yuan*, Wenjie Zhang, Zhengyi Yang
RAIDS Lab Authors
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Abstract
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.
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@article{chen2025recent,
title = {Recent Advances in Efficient Dynamic Graph Processing},
author = {Chen, Zi and Liang, Keke and Yuan, Long and Zhang, Wenjie and Yang, Zhengyi},
volume = {15},
issn = {2076-3417},
url = {http://dx.doi.org/10.3390/app15116003},
doi = {10.3390/app15116003},
number = {11},
journal = {Applied Sciences},
publisher = {MDPI AG},
year = {2025},
month = May,
pages = {6003}
}