Vector-Valued Quantitative Bipolar Argumentation for Node Classification

Node classification combines evidence from node attributes with potentially conflicting information from graph neighbors. We develop an end-to-end trainable architecture based on vector-valued quantitative bipolar argumentation frameworks (QBAFs). Feature-grounded arguments carry class-specific evidence, while dynamic support, neutral, and attack relations determine how that evidence revises neighboring arguments and class claims. Individual and jointly composed evidence contribute through the same role algebra, and prediction uses only the accumulated terminal claims. These operations expose feature contributions, relational actions, and score increments within the predictive computation itself. We prove that single-channel role interventions have class-specific spatial effects and derive downstream score bounds with conditions for preserving the predicted class. Against 16 comparison methods, QBAF achieves the highest tabulated mean on a majority of the benchmarks—six of nine datasets. Forty-eight matched ablation runs establish the contribution of local argument composition and measure the effects of relational conditioning and class weighting. Recorded traces reconstruct predictions, and controlled interventions identify evidence sustaining correct decisions or contributing to errors. The architecture connects graph learning and quantitative argumentation through a prediction mechanism whose reasoning can be reconstructed and intervened upon.