Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications
AbstractForecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions. The dataset and code have been made available online.
How to Cite
Ji, T., Self, N., Fu, K., Chen, Z., Ramakrishnan, N., & Lu, C.-T. (2021). Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 7953-7960. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16970
AAAI Technical Track on Machine Learning II