Graph Neural Networks for Misinformation Detection: Performance-Efficiency Trade-offs

ArXi:2604.08131v1 Announce Type: new The rapid spread of online misinformation has led to increasingly complex detection models, including large language models and hybrid architectures. However, their computational cost and deployment limitations raise concerns about practical applicability. In this work, we benchmark graph neural networks (GNNs) against non-graph-based machine learning methods under controlled and comparable conditions.