Proportional Selection in Networks
Oxford
arXiv:2502.03545v12 citationsh-index: 9
AI Analysis
This addresses network analysis tasks for researchers and practitioners, but appears incremental as it builds on existing selection methods.
The paper tackles the problem of selecting k representative nodes from a network to balance influence and diversity, proposing two approaches with theoretical analysis and experimental validation.
We address the problem of selecting $k$ representative nodes from a network, aiming to achieve two objectives: identifying the most influential nodes and ensuring the selection proportionally reflects the network's diversity. We propose two approaches to accomplish this, analyze them theoretically, and demonstrate their effectiveness through a series of experiments.