Improving Source Localization by Perturbing Graph Diffusion

Graph diffusion has quite common phenomenons in our daily life, such as misinformation propagation. As the inverse problem of graph diffusion, the goal of source localization is to identify those nodes of the network from which the information started to spread. Though graph diffusion has been well explored in the literature, the emerging source localization problem is important yet challenging because of its intrinsic ill-posed characteristics. While graph neural networks (GNN) are recently utilized to implement source localization and achieve state-of-the-art performance, a general GNN framework consists of two stages: feature construction and label propagation. Typically, a neural network is pretrained in the feature construction, and then combine with additional functions to jointly perform finetuning for source localization. However, those emerging methods have risks in overfitting the feature construction task, which usually has a gap with the target downstream task of source localization. Such a gap is neglected by previous methods and leads to suboptimal performance. To address this issue, we propose a very simple yet effective method to help better finetune feature construction on the source localization task by adding some noise to the parameters of the feature construction model before finetuning. More specifically, we utilize a matrix-wise perturbing method that adds different uniform noises to different parameter matrices, and design the noise considering the variances and magnitude of network weights. Extensive experiments on six real-world datasets show the proposed method can consistently empower the finetuning of different pretrained feature construction models on the downstream source localization task. Moreover, we conduct an ablation study to investigate the performance with different noise types and intensities. Code is available at: https://github.com/IndigoPurple/PGD.