Geo-Awareness of Learnt Citations Prediction for Scientific Publications (Demo Paper)
Geo-Awareness of Learnt Citations Prediction for Scientific Publications (Demo Paper)
复制标题
科学出版物学习引文预测的地理意识（演示论文）
DOI：
10.1145/3615896.3628341
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Trajcevski, Goce
中科院分区：
文献类型：
--
作者：
Li, Ce;Postler, Will;Johnson, Ian;Brinkmann, Paul;Gossling, Evan;Gorlewski, Bailey;Trajcevski, Goce
预测学术/科学出版物的引用数量最近激发了大量的研究，作为对不断发展的（异构）网络的更广泛级联预测的一个特定变体。然而，在将源数据集周围的地理社会和背景方面联系起来方面做得并不多。具体地，作为对为了各种挖掘和预测任务的目的而确定趋势的补充，更广泛的上下文方面可以帮助其他规划任务（例如，团队组建、资源分配等）。由于缺乏互动的探索与（各种粒度）空间，时间和其他元数据方面的模型预测的工具，我们采取了一个步骤，实现一个原型系统提供这样的功能。在这篇演示论文中，我们提出了一个系统的概念验证实现，对于一个给定的模型，用于预测未来的引用，使：（1）可视化探索的地理位置的机构与合作者隶属，在不同的粒度;（2）访问所需的元数据有关的作者/机构。我们使用来自APS期刊的开源数据来训练机器学习模型，以预测引用计数，并实现其他上下文查询的（可视化）。我们的系统实现的源代码是公开的。
Predicting the citation count of academic/scientific publications has recently spurred a significant amount of research, as a particular variant of the broader cascade prediction for evolving (heterogeneous) networks. However, not much has been done in terms of tying thegeo-socialandcontextualaspects surrounding the source datasets. Specifically, in complement to determining the trends for the purpose of various mining and prediction tasks, the broader contextual aspects can help in other planning tasks (e.g., teams-forming, allocations of resources, etc.). Given the lack of tools for interactive exploration of the prediction of the models in-concert with (various granularities of) spatial, temporal and other metadata aspects we took a step towards implementing a prototype system providing such functionalities. In this demonstration paper we present a proof-of-concept implementation of a system that, for a given model for predicting future citations enables: (1) Visual exploration of geo-locations of the institutions with which the co-authors are affiliated, at various granularity; and (2) Access to desired meta-data pertaining to the authors/institutions. We used the open-source data from the APS journal to train the machine learning models to predict the citation count, as well as to enable the (visualization of) other contextual queries. The source code of the implementation of our system is publicly available.
DOI：
--
发表时间：
2019
期刊：
International Conference on Computational Collective Intelligence
影响因子：
--
作者：
J. Guerrero;Víctor Hugo Menéndez Domínguez;M. Castellanos;L. Curi
通讯作者：
L. Curi