Krallman A, Pelletier MJ, Adams FG.. @ size vs.# impact: Social media engagement differences amongst facebook, twitter, and instagram. In: Celebrating America’s Pastimes: Baseball, Hot Dogs, Apple Pie and Marketing? Berlin: Springer; 2016. p. 557–61.

Newman M. Networks. Oxford: Oxford University Press; 2018.

Book
MATH
Google Scholar

Barabási A-L, et al. Network science. Cambridge: Cambridge University Press; 2016.

MATH
Google Scholar

Girvan M, Newman ME. Community structure in social and biological networks. Proc Natl Acad Sci. 2002;99(12):7821–6.

Article
MathSciNet
MATH
Google Scholar

Borgatti SP. Centrality and network flow. Soc Netw. 2005;27(1):55–71.

Article
MathSciNet
Google Scholar

Jeong H, Néda Z, Barabási A-L. Measuring preferential attachment in evolving networks. EPL (Europhys Lett). 2003;61(4):567.

Article
Google Scholar

Albert R, Barabási A-L. Statistical mechanics of complex networks. Rev Mod Phys. 2002;74(1):47.

Article
MathSciNet
MATH
Google Scholar

Laflin P, Mantzaris AV, Ainley F, Otley A, Grindrod P, Higham DJ. Discovering and validating influence in a dynamic online social network. Soc Netw Anal Min. 2013;3(4):1311–23.

Article
Google Scholar

Soares FB, Recuero R, Zago G. Influencers in polarized political networks on twitter. In: Proceedings of the 9th international conference on social media and society. 2018. p. 168–77.

Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. In: Recommender systems handbook. Berlin: Springer; 2011. p. 1–35.

Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in artificial intelligence. 2009;2009.

Unga, LH, Foster DP. Clustering methods for collaborative filtering. In: AAAI workshop on recommendation systems, Menlo Park, CA, vol. 1. 1998. p. 114–29.

Zhang R, Tran T. An information gain-based approach for recommending useful product reviews. Knowl Inf Syst. 2011;26(3):419–34.

Article
Google Scholar

Kabakchieva D. Student performance prediction by using data mining classification algorithms. Int J Comput Sci Manag Res. 2012;1(4):686–90.

Google Scholar

Thai-Nghe N, Drumond L, Krohn-Grimberghe A, Schmidt-Thieme L. Recommender system for predicting student performance. Procedia Comput Sci. 2010;1(2):2811–9.

Article
Google Scholar

Jackson MO. Networks in the understanding of economic behaviors. J Econ Perspect. 2014;28(4):3–22.

Article
Google Scholar

Shu K, Wang S, Tang J, Zafarani R, Liu H. User identity linkage across online social networks: a review. Acm Sigkdd Explor Newsl. 2017;18(2):5–17.

Article
Google Scholar

Althoff T, Jindal P, Leskovec J. Online actions with offline impact: how online social networks influence online and offline user behavior. In: Proceedings of the tenth ACM international conference on web search and data mining. 2017. p. 537–46.

Crucitti P, Latora V, Porta S. Centrality measures in spatial networks of urban streets. Phys Rev E. 2006;73(3):036125.

Article
MATH
Google Scholar

Euler L. Solutio problematis ad geometriam situs pertinentis. Commentarii academiae scientiarum Petropolitanae. 1741: 128–40.

Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2(3):203–71.

Article
Google Scholar

Belyi A, Bojic I, Sobolevsky S, Sitko I, Hawelka B, Rudikova L, Kurbatski A, Ratti C. Global multi-layer network of human mobility. Int J Geogr Inf Sci. 2017;31(7):1381–402.

Article
Google Scholar

McPherson M, Smith-Lovin L, Cook JM. Birds of a feather: homophily in social networks. Ann Rev Sociology. 2001;27(1):415–44.

Article
Google Scholar

Son J, Kim SB. Academic paper recommender system using multilevel simultaneous citation networks. Decis Support Syst. 2018;105:24–33.

Article
Google Scholar

Radicchi F, Fortunato S, Vespignani A. Citation networks. In: Models of science dynamics. Berlin: Springer; 2012. p. 233–57.

Suthaharan S. Big data classification: problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Perform Eval Rev. 2014;41(4):70–3.

Article
Google Scholar

Caldarola EG, Rinaldi AM. Big data visualization tools: a survey. Research Gate 2017.

Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS, Srivastava G, Baker T. Analysis of dimensionality reduction techniques on big data. IEEE Access. 2020;8:54776–88.

Article
Google Scholar

Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G. The graph neural network model. IEEE Trans Neural Netw. 2008;20(1):61–80.

Article
Google Scholar

Zhou J, Cui G, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: a review of methods and applications. arXiv preprint arXiv:1812.08434 2018.

Wu F, Zhang T, Souza AHd, Fifty C, Yu T, Weinberger KQ. Simplifying graph convolutional networks. In: 36th international conference on machine learning, ICML 2019, 2019-June, 2019. p. 11884–94.

Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol). 1996;58(1):267–88.

MathSciNet
MATH
Google Scholar

Mccallum A. Cora research paper classification dataset. people. cs. umass. edu/mccallum/data. html. KDD 2001.

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.

Article
Google Scholar

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.

Article
Google Scholar

Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems. 2016. p. 3844–52

Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P. The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag. 2013;30(3):83–98.

Article
Google Scholar

Sandryhaila A, Moura JM. Discrete signal processing on graphs. IEEE Trans Signal Process. 2013;61(7):1644–56.

Article
MathSciNet
MATH
Google Scholar

NT H, Maehara T. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 2019.

Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: 5th international conference on learning representations, ICLR 2017—conference track proceedings 2016.

Shchur O, Mumme M, Bojchevski A, Günnemann S. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 2018.

Newman ME. Modularity and community structure in networks. Proc Natl Acad Sci. 2006;103(23):8577–82.

Article
Google Scholar

Ketkar N. Introduction to pytorch. In: Deep learning with Python. Berlin: Springer; 2017. p. 195–208.

Bidoki NH, Mantzaris AV, Sukthankar G. Exploiting weak ties in incomplete network datasets using simplified graph convolutional neural networks. Mach Learn Knowl Extract. 2020;2(2):125–46.

Article
Google Scholar