@article{Liu2025, 
author = {Jun-Feng Liu and Shuai Ma and Han-Qing Chen},
title = {Incremental Detection of Strongly Connected Components for Scholarly Data},
year = {2025},
journal = {Journal of Computer Science and Technology},
volume = {40},
number = {5},
pages = {1468-1484},
keywords = {incremental algorithm, strongly connected component (SCC) detection, graph partition, local topological order, scholarly data analysis},
url = {https://www.sciopen.com/article/10.1007/s11390-025-4300-z},
doi = {10.1007/s11390-025-4300-z},
abstract = {Strongly connected component (SCC) detection is fundamental for analyzing citation graphs, yet existing general-purpose algorithms inefficiently handle the dynamic nature and specific properties of these networks. This study addresses this gap by developing specialized incremental SCC detection methods. We first leverage distinct edge types inherent in citation graphs to devise partition and local topological ordering strategies, minimizing redundant graph traversals. Based on this, we introduce two efficient bounded incremental algorithms: one for continuous single updates via dynamic maintenance of partitions and order, and the other for batch updates that further reduces edge traversals by building upon the single-update technique. Experimental evaluations on real-world citation graphs verify significant efficiency improvements, with our single incremental method achieving speedups of at least 11.5 times, and the batch incremental method achieving speedups of at least 5.0 times compared with baseline methods.}
}