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Citations of scientific papers and patents reveal the knowledge flow and usually serve as the metric for evaluating their novelty and impacts in the field. Citation Forecasting thus has various applications in the real world. Existing works on citation forecasting typically exploit the sequential properties of citation events, without exploring the citation network. In this paper, we propose to explore both the citation network and the related citation event sequences which provide valuable information for future citation forecasting. We propose a novel Citation Network and Event Sequence (CINES) Model to encode signals in the citation network and related citation event sequences into various types of embeddings for decoding to the arrivals of future citations. Moreover, we propose a temporal network attention and three alternative designs of bidirectional feature propagation to aggregate the retrospective and prospective aspects of publications in the citation network, coupled with the citation event sequence embeddings learned by a two-level attention mechanism for the citation forecasting. We evaluate our models and baselines on both a U.S. patent dataset and a DBLP dataset. Experimental results show that our models outperform the state-of-the-art methods, i.e., RMTPP, CYAN-RNN, Intensity-RNN, and PC-RNN, reducing the forecasting error by 37.76% - 75.32%.
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What Types of Novelty Are Most Disruptive?
Novelty and impact are key characteristics of the scientific enterprise. Classic theories of scientific change distinguish among different types of novelty and emphasize how a new idea interacts with previous work and influences future flows of knowledge. However, even recently developed measures of novelty remain unidimensional, and continued reliance on citation counts captures only the amount, but not the nature, of scientific impact. To better align theoretical and empirical work, we attend to different types of novelty (new results, new theories, and new methods) and whether a scientific offering has a consolidating form of influence (bringing renewed attention to foundational ideas) or a disruptive one (prompting subsequent scholars to overlook them). By integrating data from the Web of Science (to measure the nature of influence) with essays written by authors of Citation Classics (to measure novelty type), and by joining computational text analysis with statistical analyses, we demonstrate clear and robust patterns between type of novelty and the nature of scientific influence. As expected, new methods tend to be more disruptive, whereas new theories tend to be less disruptive. Surprisingly, new results do not have a robust effect on the nature of scientific influence.
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- Award ID(s):
- 1829168
- PAR ID:
- 10413136
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- American Sociological Review
- Volume:
- 88
- Issue:
- 3
- ISSN:
- 0003-1224
- Format(s):
- Medium: X Size: p. 562-597
- Size(s):
- p. 562-597
- Sponsoring Org:
- National Science Foundation
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