  Evaluating node influence is fundamental for identifying key nodes in complex
networks. Existing methods typically rely on generic indicators to rank node
influence across diverse networks, thereby ignoring the individualized features
of each network itself. Actually, node influence stems not only from general
features but the multi-scale individualized information encompassing specific
network structure and task. Here we design an active learning architecture to
predict node influence quantitively and precisely, which samples representative
nodes based on graph entropy correlation matrix integrating multi-scale
individualized information. This brings two intuitive advantages: (1)
discovering potential high-influence but weak-connected nodes that are usually
ignored in existing methods, (2) improving the influence maximization strategy
by deducing influence interference. Significantly, our architecture
demonstrates exceptional transfer learning capabilities across multiple types
of networks, which can identify those key nodes with large disputation across
different existing methods. Additionally, our approach, combined with a simple
greedy algorithm, exhibits dominant performance in solving the influence
maximization problem. This architecture holds great potential for applications
in graph mining and prediction tasks.
