Incremental Detection of Strongly Connected Components for Scholarly Data

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 another 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 to baseline methods.