The official Pytorch implementation of DMA-Nets proposed in paper "Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications", AAAI 2021.
Install torch, numpy, python-dateutil, and tqdm first. Run bash dma-nets.sh for a quickstart. See the first couple of lines of dma_nets.sh for examples.
bash dma_nets.sh [gpu to use] [dataset to use]Title: Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications
Abstract: Forecasting 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.
If you used data or codes in this repo in your research, please cite "Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications".
@article{Ji_Self_Fu_Chen_Ramakrishnan_Lu_2021,
title={Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications},
volume={35},
url={https://ojs.aaai.org/index.php/AAAI/article/view/16970},
number={9},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Ji, Taoran and Self, Nathan and Fu, Kaiqun and Chen, Zhiqian and Ramakrishnan, Naren and Lu, Chang-Tien},
year={2021},
month={May},
pages={7953-7960}
}Codes in this repo released under MIT license.
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