A. Hasan, M. Chaoji, V. Salem, S. Zaki, and M. , Link prediction using supervised learning, SDM06: workshop on link analysis, counter-terrorism and security, 2006.

A. Hasan, M. Zaki, and M. J. , A survey of link prediction in social networks, Social network data analytics, pp.243-275, 2011.

A. L. Barabási, Scale-free networks: a decade and beyond, science, vol.325, issue.5939, pp.412-413, 2009.

M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in neural information processing systems, pp.585-591, 2002.

R. Berk, H. Heidari, S. Jabbari, M. Kearns, and A. Roth, Fairness in criminal justice risk assessments: the state of the art, 2017.

S. Bhagat, G. Cormode, and S. Muthukrishnan, Node classification in social networks, Social network data analytics, pp.115-148, 2011.

V. D. Blondel, J. L. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of statistical mechanics: theory and experiment, issue.10, p.8, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01146070

T. Bolukbasi, K. W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai, Man is to computer programmer as woman is to homemaker? debiasing word embeddings, Advances in Neural Information Processing Systems, pp.4349-4357, 2016.

H. Cai, V. W. Zheng, and K. Chang, A comprehensive survey of graph embedding: problems, techniques and applications, IEEE Transactions on Knowledge and Data Engineering, 2018.

A. Clauset, C. Moore, and M. E. Newman, Hierarchical structure and the prediction of missing links in networks, Nature, vol.453, issue.7191, p.98, 2008.

P. Cui, X. Wang, J. Pei, and W. Zhu, A survey on network embedding, IEEE Transactions on Knowledge and Data Engineering, 2018.

S. Fortunato, Community detection in graphs, Physics reports, vol.486, issue.3-5, pp.75-174, 2010.

P. Goyal and E. Ferrara, Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Systems, vol.151, pp.78-94, 2018.

A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.855-864, 2016.

J. Leskovec, J. Kleinberg, and C. Faloutsos, Graph evolution: Densification and shrinking diameters, ACM Transactions on Knowledge Discovery from Data (TKDD), vol.1, issue.1, 2007.

J. Leskovec and J. J. Mcauley, Learning to discover social circles in ego networks, Advances in neural information processing systems, pp.539-547, 2012.

R. N. Lichtenwalter, J. T. Lussier, and N. V. Chawla, New perspectives and methods in link prediction, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.243-252, 2010.
DOI : 10.1145/1835804.1835837
URL : http://www.cse.nd.edu/~nchawla/papers/KDD10.pdf

L. Lü and T. Zhou, Link prediction in complex networks: A survey, Physica A: statistical mechanics and its applications, vol.390, issue.6, pp.1150-1170, 2011.

M. Ou, P. Cui, J. Pei, Z. Zhang, and W. Zhu, Asymmetric transitivity preserving graph embedding, Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1105-1114, 2016.
DOI : 10.1145/2939672.2939751

E. Pariser, The filter bubble: What the Internet is hiding from you, 2011.

L. F. Ribeiro, P. H. Saverese, and D. R. Figueiredo, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.385-394, 2017.

G. Rossetti and R. Cazabet, Community discovery in dynamic networks: a survey, ACM Computing Surveys (CSUR), vol.51, issue.2, p.35, 2018.
DOI : 10.1145/3172867
URL : https://hal.archives-ouvertes.fr/hal-01658399

A. Tsitsulin, D. Mottin, P. Karras, and E. Müller, Verse: Versatile graph embeddings from similarity measures, Proceedings of the 2018 World Wide Web Conference on World Wide Web, pp.539-548, 2018.

J. Yang and J. Leskovec, Community-affiliation graph model for overlapping network community detection, 2012 IEEE 12th International Conference on Data Mining, pp.1170-1175, 2012.
DOI : 10.1109/icdm.2012.139

Y. Yang, R. N. Lichtenwalter, and N. V. Chawla, Evaluating link prediction methods, Information Systems, vol.45, issue.3, pp.751-782, 2015.
DOI : 10.1007/s10115-014-0789-0
URL : http://arxiv.org/pdf/1505.04094