Abstract: Social network is the main channel of information dissemination,and identifying key nodes in social networks is important for discovering information dissemination hubs and performing information dissemination control.Realistic social networks are time-varying,and reasonable modeling of temporal networks with comprehensive description and deep mining of the spatial and temporal relationships of nodes is an important factor for accurately identifying key nodes in the network.In order to improve the accuracy of key node identification,a deep learning and multi-feature fusion based method MCNN(Multidimensional CNN) for key node identification in temporal social networks is proposed.The method firstly models the temporal network as a multidimensional relational network based on snapshots,and for a node,in each snapshot,the spatial,temporal,and spatio-temporal contexts of the node are extracted from the spatial structure,temporal coupling,and three types of spatio-temporal propagation relations,respectively,and the node feature matrix is constructed.In order to deeply analyze the spatio-temporal relationships of nodes in each snapshot,three types of node features are extracted using CNN,respectively,and fused to form the node snapshot features using the self-attention mechanism.To capture the evolution of node behaviors between snapshots,node snapshot features of all snapshots are combined as a time sequence,and LSTM is used to mine the snapshot sequence features.Finally,the influence of nodes is predicted using a fully connected layer.Experiments on six real temporal social networks show that MCNN outperforms the baseline approaches for key node identification in temporal social networks.

Key words: Temporal social network, Node influence, Network representation, Spatio-temporal characteristics, Deep learning