CLG: A Multi-Feature Node Influence Ranking Framework for Software Networks

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 groundtruth correlation analyses. Experiments on five open-source software networks show our method consistently outperforms 14 baseline approaches across key metrics.