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CLG: A Multi-Feature Node Influence Ranking Framework for Software Networks
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DOI：10.1142/s0218194026500257.png)
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Abstract
En 中文
Measuring node influence in software networks is critical for identifying key functional components. However, existing methods rely on limited structural features and lack robust validation. We propose a novel node ranking approach (CLG) that integrates structural hole theory to capture network Constraint, defect aggregation and propagation analysis to represent Local fault impact, and length-penalized shortest path centrality to assess Global information dissemination. Validation employs a dual framework: Ranking capability metrics (distinctness, monotonicity) assess node discrimination and ranking consistency; Correctness verification combines SIR epidemic simulations with ground-truth correlation analyses. Experiments on five open-source software networks show our method consistently outperforms 14 baseline approaches across key metrics.
Keywords:
Software network
influential node
ranking method
SIR model
Journal
I
IF:
0.6
Papers:
106
Citations:
543