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Social network embedding, namely, embedding social network nodes into a low-dimensional space, is the foundation of social network analysis, such as node classification and link prediction. Although many existing methods attempt to address this task, most of them only consider the shallow relationship between two nodes in the network, which ignore capturing multiple and semantic-rich social relationships between users. To this end, we define such multiple and semantic-rich relationships as multi-path relationships, and propose a multi-path relationship preserved social network embedding method named MPR-SNE, which is based on the recurrent neural network framework that incorporates both social network structure and node profile information. Specifically, we first utilize random walks to explore the multiple social relationship paths between nodes. Then, a new recurrent unit called bi-directional multi-path relationship unit is proposed to better capture the properties of multi-path relationships. Finally, two objective functions are designed to seamlessly integrate social network structure and node profile information into node representation. The experimental results on two real-world networks show that MPR-SNE outperforms the state-of-the-art baselines on node classification task and link prediction task. INDEX TERMS Social network embedding, multi-path relationship, node profile information, RNN.

In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning of risks. This report proposes a real-time ship vertical acceleration prediction algorithm based on the long short-term memory (LSTM) and gated recurrent units (GRU) models of a recurrent neural network. The vertical acceleration time history data at the bow, middle, and stern of a large-scale ship model were obtained by performing a self-propulsion test at sea, and the original data were pre-processed by resampling and normalisation via Python. The prediction results revealed that the proposed algorithm could accurately predict the acceleration time history data of the large-scale ship model, and the root mean square error between the predicted and real values was no greater than 0.1. The optimised multivariate time series prediction program could reduce the calculation time by approximately 55% compared to that of a univariate time series prediction program, and the run time of the GRU model was better than that of the LSTM model.

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