Kinship Determination in Mobile Social Networks

With the progress of mobile communication technology, mobile social networks that can reflect the real situation of human social activities in real life have gradually formed. An even challenging problem whether it is possible to determine the kinship merely based on mobile social networks is raised during research on mobile social networks. This enlightens us the following research. First, we take the mobile phone contacts, telephone network and SMS network as the object of social network research. Second, we tackle the challenge of kinship mining using novel feature extraction and selection methods. Third, after selecting the most discriminative data features, we use a variety of algorithms to compare the study of the kinship in this paper. Finally, we demonstrate a classification accuracy of 81.04% on a test set using XGBoost.

[1]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[2]  Mark S. Granovetter T H E S T R E N G T H O F WEAK TIES: A NETWORK THEORY REVISITED , 1983 .

[3]  Kenton O'Hara,et al.  Social Impact , 2019, Encyclopedia of Food and Agricultural Ethics.

[4]  Hassan Khosravi,et al.  Transaction-based link strength prediction in a social network , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[5]  M. Newman Communities, modules and large-scale structure in networks , 2011, Nature Physics.

[6]  Mark E. J. Newman,et al.  Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, ArXiv.

[7]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[8]  Vincent Y. Shen,et al.  User identification across multiple social networks , 2009, 2009 First International Conference on Networked Digital Technologies.

[9]  Eric Gilbert,et al.  Predicting tie strength with social media , 2009, CHI.

[10]  Jie Tang,et al.  Learning to Infer Social Ties in Large Networks , 2011, ECML/PKDD.

[11]  Jennifer Neville,et al.  Modeling relationship strength in online social networks , 2010, WWW '10.

[12]  Matthew Richardson,et al.  Yes, there is a correlation: - from social networks to personal behavior on the web , 2008, WWW.

[13]  Hua Qian Social relationships in blog webrings , 2008 .

[14]  Rich Caruana,et al.  An empirical comparison of supervised learning algorithms , 2006, ICML.

[15]  M. Newman Community detection in networks: Modularity optimization and maximum likelihood are equivalent , 2016, Physical review. E.

[16]  Chuang Liu,et al.  Epidemic Spreading on Weighted Complex Networks , 2013, ArXiv.

[17]  Joaquin Quiñonero Candela,et al.  Practical Lessons from Predicting Clicks on Ads at Facebook , 2014, ADKDD'14.

[18]  Marcela Perrone-Bertolotti,et al.  Machine learning–XGBoost analysis of language networks to classify patients with epilepsy , 2017, Brain Informatics.

[19]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .