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=== 代表性论文 ===

=== 代表性论文 ===

*Lifei Wang et al.: An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data; Nature Machine Intelligence, 2: 693703(2020) 这篇文章开发了一种可解释的胶囊网络深度学习架构(scCapsNet)，在此基础上，使用多个scRNA-seq数据集评估scCapsNet为单细胞转录组分析指定的值。利用二维主成分分析(PCA)对特征提取层内权值参数进行分析。通过竞争性单细胞类型识别，scCapsNet模型能够进行特征选择来识别编码不同亚细胞类型的基因组，使亚细胞型识别成为可能的RNA表达特征被有效地集成到scCapsNet的参数矩阵中。这一特性使基因调控模块的发现成为可能。

*Lifei Wang et al.: "[https://www.nature.com/articles/s42256-020-00244-4 An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data]; Nature Machine Intelligence, 2: 693703(2020) 这篇文章开发了一种可解释的胶囊网络深度学习架构(scCapsNet)，在此基础上，使用多个scRNA-seq数据集评估scCapsNet为单细胞转录组分析指定的值。利用二维主成分分析(PCA)对特征提取层内权值参数进行分析。通过竞争性单细胞类型识别，scCapsNet模型能够进行特征选择来识别编码不同亚细胞类型的基因组，使亚细胞型识别成为可能的RNA表达特征被有效地集成到scCapsNet的参数矩阵中。这一特性使基因调控模块的发现成为可能。

*Zhang, Z., Zhao, Y., Liu, J. et al. [https://link.springer.com/article/10.1007/s41109-019-0194-4#citeas A general deep learning framework for network reconstruction and dynamics learning]. Appl Netw Sci 4, 110 (2019). https://doi.org/10.1007/s41109-019-0194-4<ref name="GNN">Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin & Jiang Zhang (2019) [https://pattern.swarma.org/paper?id=199529de-337f-11ea-b58a-0242ac1a0005 A general deep learning framework for network reconstruction and dynamics learning].</ref>

 

 

*Zhang, Z., Zhao, Y., Liu, J. et al. [https://link.springer.com/article/10.1007/s41109-019-0194-4#citeas A general deep learning framework for network reconstruction and dynamics learning]. Appl Netw Sci 4, 110 (2019). https://doi.org/10.1007/s41109-019-0194-4<ref name="GNN">Zhang Zhang, Yi Zhao, Jing Liu, Shuo Wang, Ruyi Tao, Ruyue Xin & Jiang Zhang (2019) [https://pattern.swarma.org/paper?id=199529de-337f-11ea-b58a-0242ac1a0005 A general deep learning framework for network reconstruction and dynamics learning].</ref>:这篇文章提出了一套全新的网络和动力学重构的算法。即仅根据时间序列数据，就能够重构整个原始的相互作用网络以及每个节点的动力学，这相当于对原系统进行了自动建模。该算法不仅在精确度上远超其它对比算法，而且可以广泛地适用于各类系统。于2019年发表在Applied Network Science上。相关的论文解读资料可以查看[https://mp.weixin.qq.com/s/O9Q81ebX7DKEUIhvc7r5kQ 超越简单规则——用图神经网络对复杂系统进行自动建模]

* Li, R., Dong, L., Zhang, J., Wang, X., Wang, W. X., & Di, Z & Stanley, H. E. (2017). "[https://www.nature.com/articles/s41467-017-01882-w.pdf Simple spatial scaling rules behind complex cities]". Nature Communications, 8(1), 1841.  <ref name="rules">Ruiqi Li,Lei Dong,Jiang Zhang,Xinran Wang,Wen-Xu Wang,Zengru Di,H. Eugene Stanley (2017) [https://pattern.swarma.org/paper?id=ff73afb2-3838-11ea-bb46-0242ac1a0005 Simple spatial scaling rules behind complex cities].</ref>相关的论文解读资料可以查看[https://mp.weixin.qq.com/s/O9Q81ebX7DKEUIhvc7r5kQ 张江：从规模理论，看城市的生长、创新与奇点]

* Li, R., Dong, L., Zhang, J., Wang, X., Wang, W. X., & Di, Z & Stanley, H. E. (2017). "[https://www.nature.com/articles/s41467-017-01882-w.pdf Simple spatial scaling rules behind complex cities]". Nature Communications, 8(1), 1841.  <ref name="rules">Ruiqi Li,Lei Dong,Jiang Zhang,Xinran Wang,Wen-Xu Wang,Zengru Di,H. Eugene Stanley (2017) [https://pattern.swarma.org/paper?id=ff73afb2-3838-11ea-bb46-0242ac1a0005 Simple spatial scaling rules behind complex cities].</ref>相关的论文解读资料可以查看[https://mp.weixin.qq.com/s/O9Q81ebX7DKEUIhvc7r5kQ 张江：从规模理论，看城市的生长、创新与奇点]

:这篇文章不仅回答了宏观规模标度律的起源，而且可以精准预测人口、GDP、道路网络等城市要素的空间分布情况。于2017年发表在Nature Communications上，该文总引用数为40次。相关的论文解读资料可以查看[https://mp.weixin.qq.com/s/O9Q81ebX7DKEUIhvc7r5kQ 超越简单规则——用图神经网络对复杂系统进行自动建模]

:这篇文章不仅回答了宏观规模标度律的起源，而且可以精准预测人口、GDP、道路网络等城市要素的空间分布情况。于2017年发表在Nature Communications上。

* Jiang Zhang, Xintong Li, Xinran Wang, Wenxu Wang, Lingfei Wu: [http://swarmagents.cn.13442.m8849.cn/thesis/doc/jake_379.pdf Scaling behaviours in the growth of networked systems and their geometric origins], Scientific Reports 2015, 5: 9767.<ref name="scale">Jiang Zhang,Xintong Li,Xinran Wang,Wen-Xu Wang,Lingfei Wu (2015) [https://pattern.swarma.org/paper?id=ad6da34e-455d-11ea-ac18-0242ac1a0005 Scaling behaviours in the growth of networked systems and their geometric origins].</ref>

* Jiang Zhang, Xintong Li, Xinran Wang, Wenxu Wang, Lingfei Wu: [http://swarmagents.cn.13442.m8849.cn/thesis/doc/jake_379.pdf Scaling behaviours in the growth of networked systems and their geometric origins], Scientific Reports 2015, 5: 9767.<ref name="scale">Jiang Zhang,Xintong Li,Xinran Wang,Wen-Xu Wang,Lingfei Wu (2015) [https://pattern.swarma.org/paper?id=ad6da34e-455d-11ea-ac18-0242ac1a0005 Scaling behaviours in the growth of networked systems and their geometric origins].</ref>

:这篇文章主要提出了一种几何网络模型，该模型可以很好地对社会经济系统中的缩放行为进行解释。于2017年发表在Scientific Reports上，该文总引用数为31次。

:这篇文章主要提出了一种几何网络模型，该模型可以很好地对社会经济系统中的缩放行为进行解释。于2017年发表在Scientific Reports上。

* Shi P, Zhang J, Yang B, et al. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098247 Hierarchicality of trade flow networks reveals complexity of products][J]. PloS one, 2014, 9(6).<ref name="">Peiteng Shi, Jiang Zhang, Bo Yang, Jingfei Luo (2014) [https://pattern.swarma.org/paper?id=f109684e-78a8-11ea-873b-0242ac1a0005 Hierarchicality of trade flow networks reveals complexity of products].PLOS ONE.</ref>

* Shi P, Zhang J, Yang B, et al. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0098247 Hierarchicality of trade flow networks reveals complexity of products][J]. PloS one, 2014, 9(6).<ref name="">Peiteng Shi, Jiang Zhang, Bo Yang, Jingfei Luo (2014) [https://pattern.swarma.org/paper?id=f109684e-78a8-11ea-873b-0242ac1a0005 Hierarchicality of trade flow networks reveals complexity of products].PLOS ONE.</ref>

:这篇文章通过计算贸易流网络的规模标度律从而利用规模标度律指数刻画不同产品的复杂性程度，即价值生产附加情况。另一方面，给每个国家针对某一产品的国际贸易网计算出其对其它国家整体的影响力。于2014年发表在PLOS ONE上，该文总引用数为26次。

:这篇文章通过计算贸易流网络的规模标度律从而利用规模标度律指数刻画不同产品的复杂性程度，即价值生产附加情况。另一方面，给每个国家针对某一产品的国际贸易网计算出其对其它国家整体的影响力。于2014年发表在PLOS ONE上。

* Jiang Zhang, Tongkui Yu, [https://www.sciencedirect.com/science/article/pii/S0378437110006072 Allometric Scaling of Countries], Physica A 389 (2010) 4887-4896 (SCI,EI)<ref name="scaling">Jiang Zhang, Tongkui Yu (2010) [https://pattern.swarma.org/paper?id=359d4b7a-78a8-11ea-b30c-0242ac1a0005 Allometric scaling of countries].Physica A: Statistical Mechanics and its Applications.389.21:(4887-4896)</ref>

* Jiang Zhang, Tongkui Yu, [https://www.sciencedirect.com/science/article/pii/S0378437110006072 Allometric Scaling of Countries], Physica A 389 (2010) 4887-4896 (SCI,EI)<ref name="scaling">Jiang Zhang, Tongkui Yu (2010) [https://pattern.swarma.org/paper?id=359d4b7a-78a8-11ea-b30c-0242ac1a0005 Allometric scaling of countries].Physica A: Statistical Mechanics and its Applications.389.21:(4887-4896)</ref>

:这篇文章该文主要研究了国家的大量宏观属性与地理（区域），人口（人口）和经济（GDP，国内生产总值）大小之间的异度缩放比例关系。于2010年发表在Physica A上，总引用次数30次。

:这篇文章该文主要研究了国家的大量宏观属性与地理（区域），人口（人口）和经济（GDP，国内生产总值）大小之间的异度缩放比例关系。于2010年发表在Physica A上。

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