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Simultaneously Captures Node-Level and Sequence-Level Features in Parallel for Cascade Prediction
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DOI：10.3390/electronics15010159.png)
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Abstract
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Predicting information diffusion in social networks is a fundamental problem in many applications, and one of the primary challenges is to predict the future popularity of information in social networks. However, most existing models fail to simultaneously capture the accurate micro-level user node features, meso-level linear spread features, and predict the macro-level popularity during the information propagation process, which may result in unsatisfactory prediction performance. To address this issue, we propose a new cascade prediction framework CasNS: Node-level and Sequence-level Features for Cascade Prediction. CasNS utilizes node-level features by employing a self-attention mechanism to capture the hidden features of the target node with respect to other nodes. Additionally, it leverages multiple one-dimensional convolutional layers with the dynamic routing algorithm to obtain sequence-level features across different dimensions. Through experiments on a large number of real-world datasets, our model demonstrates superior performance compared with other state-of-the-art methods, thereby validating the feasibility of our approach.
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