Spatial Temporal Graph Convolutional Network Model for Rumor Source Detection Under Multiple Observations in Social Networks

Rumor source detection has long been an important but difficult problem. Most existing methods only rely on the limit observation of a single batch of single snapshot during the propagation process in the spatial graph networks, which neglects temporal dependency and temporal features of the rumor propagation process. Taking multiple batches of multiple snapshots as input can reveal the temporal dependency. Inspired by the traditional spatial-temporal graph convolution network (STGCN), which is a model can combine the spatial and temporal feature. In this paper, we propose an STGCN based model called Spatio-Temporal Approximate Personalized Propagation of Neural Predictions (STAPPNP), which firstly learns both the spatial and temporal features automatically from multiple batches of multiple snapshots to locate the rumor source. As there are no input algorithms which are suitable for multiple batches of multiple snapshots to capture the feature of nodes’ connectivity in STAPPNP, we develop an input algorithm to generate a 4-dimensional input matrix from the multiple batches of multiple snapshots to feed the proposed model. Nonetheless, for deep learning models, such input of multiple batches of multiple snapshots results in a long training time as the number of model layers gets large. To address these issues, we improve the Spatio-Temporal-Convolutional(ST-Conv) block, in which we adopt the approximate personalized propagation of neural predictions in the spatial convolutional layer of STAPPNP. Our experimental results show that the accuracy of the source detection is improved by using STAPPNP, and the speed of the training process of STAPPNP outperforms state-of-the-art deep learning approaches under the popular epidemic susceptible-infected (SI) and susceptible-infected-recovery (SIR) model in social networks.