Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
- Ma, Xiaoke & Sun, Penggang & Wang, Yu, 2018. "Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 121-136.
- Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
- Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
- Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Nazim Choudhury & Shahadat Uddin, 2016. "Time-aware link prediction to explore network effects on temporal knowledge evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 745-776, August.
- Zhang, Ting & Zhang, Kun & Li, Xun & Lv, Laishui & Sun, Qi, 2021. "Semi-supervised link prediction based on non-negative matrix factorization for temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
- Chen, Guangfu & Xu, Chen & Wang, Jingyi & Feng, Jianwen & Feng, Jiqiang, 2020. "Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
- Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
- Lv, Laishui & Bardou, Dalal & Hu, Peng & Liu, Yanqiu & Yu, Gaohang, 2022. "Graph regularized nonnegative matrix factorization for link prediction in directed temporal networks using PageRank centrality," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
- Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.
- Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).
- Chi, Kuo & Qu, Hui & Yin, Guisheng, 2022. "Link prediction for existing links in dynamic networks based on the attraction force," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
- Yueran Duan & Qing Guan, 2021. "Predicting potential knowledge convergence of solar energy: bibliometric analysis based on link prediction model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3749-3773, May.
- Silva, Thiago Christiano & Wilhelm, Paulo Victor Berri & Amancio, Diego R., 2024.
"Machine learning and economic forecasting: The role of international trade networks,"
Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 649(C).
- Thiago Christiano Silva & Paulo Victor Berri Wilhelm & Diego Raphael Amancio, 2024. "Machine Learning and Economic Forecasting: the role of international trade networks," Working Papers Series 597, Central Bank of Brazil, Research Department.
- Thiago C. Silva & Paulo V. B. Wilhelm & Diego R. Amancio, 2024. "Machine learning and economic forecasting: the role of international trade networks," Papers 2404.08712, arXiv.org.
- Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
- Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
- Hayashi, Masayoshi, 2014.
"Forecasting welfare caseloads: The case of the Japanese public assistance program,"
Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
- Masayoshi Hayashi, 2012. "Forecasting Welfare Caseloads: The Case of the Japanese Public Assistance Program," CIRJE F-Series CIRJE-F-846, CIRJE, Faculty of Economics, University of Tokyo.
- Gustavo Peralta, 2016. "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers CNMV Working Papers no. 6, CNMV- Spanish Securities Markets Commission - Research and Statistics Department.
- Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
- Man Li & Tao Ye & Peijun Shi & Jian Fang, 2015. "Impacts of the global economic crisis and Tohoku earthquake on Sino–Japan trade: a comparative perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 541-556, January.
- Caglar, Abdullah Emre & Daştan, Muhammet & Avci, Salih Bortecine, 2024. "Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach," Applied Energy, Elsevier, vol. 365(C).
- Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
- Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
- Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
More about this item
Keywords
social network analysis; dynamic networks; link prediction; community detection; actor dynamicity;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:285-:d:1319693. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.