Constructing Structured Decision Sources for Consensus-Based Pseudo-Label Learning
The study investigates constructing decision sources by systematically altering graph structure parameters like center granularity and neighborhood mixing to improve pseudo-label precision in consensus learning. By creating and selecting stable, complementary sources, the method significantly boosts conditional precision on citation networks, demonstrating that source construction is a key design problem for better label quality.