Centrality informed embedding of networks for temporal feature extraction

• Akcora C, Kantarcioglu M, Gel Y (2018) Blockchain data analytics. In: IEEE International Conference on Data Mining (ICDM)

• Batagelj V, Praprotnik S (2016) An algebraic approach to temporal network analysis based on temporal quantities. Soc Netw Anal Min 6(28):1–22

  MATH  Google Scholar 

• Bourgain J (1985) On Lipschitz embedding of finite metric spaces in Hilbert space. Isr J Math 52(1–2):46–52

  Article  MathSciNet  Google Scholar 

• Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques and applications. IEEE Trans Knowl Data Eng 30:1616–1637

  Article  Google Scholar 

• Estrada E (2011) The structure of complex networks: theory and applications. Oxford University Press Inc., Oxford

  Book  Google Scholar 

• Fraiman D, Fraiman N, Fraiman R (2017) Nonparametric statistics of dynamic networks with distinguishable nodes. Test 26(3):546–573

  Article  MathSciNet  Google Scholar 

• Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl-Based Syst 151:78–94

  Article  Google Scholar 

• Goyal P, Kamra N, He X, Liu Y (2018) Dyngem: deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273

• Harrigan M, Fretter C (2016) The unreasonable effectiveness of address clustering. Tech. Rep. arXiv:1605.06369, arXiv

• Holme P, Saramäki J (eds) (2013) Temporal networks. Springer, Berlin

  Google Scholar 

• Klimt B, Yang Y (2004) The enron corpus: a new dataset for email classification research. In: European Conference on Machine Learning

• Kondor D, Csabai I, Szüle J, Pósfai M, Vattay G (2014) Inferring the interplay between network structure and market effects in bitcoin. New J Phys 16:125003

  Article  Google Scholar 

• Narayanan A, Chandramohan M, Venkatesan R, Chen L, Liu Y, Jaiswal S (2017) graph2vec: learning distributed representations of graphs. Workshop on mining and learning with graphs

• Park Y, Priebe CE, Youssef A (2013) Anomaly detection in time series of graphs using fusion of graph invariants. IEEE J Sel Top Signal Process 7(1):67–75

  Article  Google Scholar 

• Peel L, Clauset A (2015) Detecting change points in the large-scale structure of evolving networks. In: Proceedings of the twenty-ninth AAAI conference on artifical intelligence

• Sarkar S, Guo R, Shakarian P (2019) Using network motifs to characterize temporal network evolution leading to diffusion inhibition. Soc Netw Anal Min 9(1):14

  Article  Google Scholar 

• Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–524

  Article  Google Scholar 

• Ye C, Wilson RC, Hancock ER (2018) Network analysis using entropy component analysis. J Complex Netw 6(3):404–429

  Article  MathSciNet  Google Scholar 

• Zhu L, Guo D, Yin J, Ver Steeg G, Galstyan A (2016) Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans Knowl Data Eng 28(10):2765–2777

  Article  Google Scholar 

Download references