%0 Journal Article %1 WOS:000318470600008 %A Muchnik, Lev %A Pei, Sen %A Parra, Lucas C %A Reis, Saulo D S %A Jr., Jose S Andrade %A Havlin, Shlomo %A Makse, Hernan A %C MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND %D 2013 %I NATURE PUBLISHING GROUP %J SCIENTIFIC REPORTS %K imported %R 10.1038/srep01783 %T Origins of power-law degree distribution in the heterogeneity of human activity in social networks %V 3 %X The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a ``maximum entropy attachment'' model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.

@article{WOS:000318470600008, abstract = {The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a ``maximum entropy attachment'' model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.}, added-at = {2022-05-23T20:00:14.000+0200}, address = {MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND}, author = {Muchnik, Lev and Pei, Sen and Parra, Lucas C and Reis, Saulo D S and Jr., Jose S Andrade and Havlin, Shlomo and Makse, Hernan A}, biburl = {https://www.bibsonomy.org/bibtex/2a1ad1d8f9b430b03bce429702a9feafc/ppgfis_ufc_br}, doi = {10.1038/srep01783}, interhash = {22365b52d3fc2830a72929715d3cc608}, intrahash = {a1ad1d8f9b430b03bce429702a9feafc}, issn = {2045-2322}, journal = {SCIENTIFIC REPORTS}, keywords = {imported}, publisher = {NATURE PUBLISHING GROUP}, pubstate = {published}, timestamp = {2022-05-23T20:00:14.000+0200}, title = {Origins of power-law degree distribution in the heterogeneity of human activity in social networks}, tppubtype = {article}, volume = 3, year = 2013 }