An Adaptive Non-Linear Graph Filter in Semi-Supervised Graph Based Classification

Lang He, Xinchun Yu, Xiao-Ping Zhang, Konstantinos N. Plataniotis

Abstract: Overcoming class imbalance is a critical challenge for graph-based semi-supervised classification methods. In this letter, we address this issue from the perspective of graph filtering and propose a novel adaptive graph filter. By introducing learnable thresholds into the adjacency matrix, the filter enables dynamic suppression of majority-class bias during label propagation through the incorporation of discontinuities. Additionally, we develop a modified Fruit Fly Optimization Algorithm (m-FOA) to optimize the filter’s coefficients, which achieves lower loss and faster convergence compared to other heuristic algorithms. To evaluate the effectiveness of our approach, we conduct a Monte Carlo simulation on a real-world dataset. The results demonstrate that our method outperforms the baseline methods in both classification accuracy and efficiency when handling class imbalance. We note that the model’s scalability to very large graphs is limited and the solving procedure can be time-consuming due to the dense construction of the filter.

External IDs:dblp:journals/spl/HeYZP26