Simplex Pattern Prediction Based on Dynamic Higher Order Path Convolutional Networks

Recently, higher order patterns have played an important role in network structure analysis. The simplices in higher order patterns enrich dynamic network modeling and provide strong structural feature information for feature learning. However, the disorder dynamic network with simplex patterns has not been organized and divided according to time windows. Besides, existing methods do not make full use of the feature information to predict the simplex patterns with higher orders. To address these issues, we propose a simplex pattern prediction method based on dynamic higher order path convolutional networks. First, we divide the dynamic higher order datasets into different network structures under continuous-time windows, which possess complete time information. Second, feature extraction is performed on the network structure of continuous-time windows through higher order path convolutional networks. Subsequently, we embed time nodes into feature encoding and obtain feature representations of simplex patterns through feature fusion. The obtained feature representations of simplices are recognized by a simplex pattern discriminator to predict the simplex patterns at different moments. Finally, compared to other dynamic graph representation learning algorithms, our proposed algorithm has significantly improved its performance in predicting simplex patterns on five real dynamic higher order datasets.