Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Computer Science - Social and Information Networks FOS: Computer and information sciences Artificial Intelligence Social and Information Networks (cs.SI) Homophily Computer Networks and Communications Attributed networks Mixing patterns
Unveiling the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works have underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Different from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.
Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
Publisher: IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America
[2] A.-L. Barabási and R. Albert, “Emergence of scaling in random networks,” science, vol. 286, no. 5439, pp. 509- 512, 1999.
[3] S. Fortunato and D. Hric, “Community detection in networks: A user guide,” Physics reports, vol. 659, pp. 1-44, 2016.
[4] M. E. Newman, “Mixing patterns in networks,” Physical Review E, 2003.
[5] L. Peel, J.-C. Delvenne, and R. Lambiotte, “Multiscale mixing patterns in networks,” Proceedings of the National Academy of Sciences, 2018.
[6] E. Estrada, N. Hatano, and A. Gutierrez, “'clumpiness' mixing in complex networks,” Journal of Statistical Mechanics: Theory and Experiment, 2008.
[7] M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks,” Annual review of sociology, 2001.
[8] J. Moody, “Race, school integration, and friendship segregation in america,” American journal of Sociology, vol. 107, no. 3, pp. 679-716, 2001.
[9] W. Shrum, N. H. Cheek Jr, and S. MacD, “Friendship in school: Gender and racial homophily,” Sociology of Education, pp. 227-239, 1988.
[10] S. Feng and A. Kirkley, “Mixing patterns in interdisciplinary co-authorship networks at multiple scales,” Scientific Reports, vol. 10, no. 1, pp. 1-11, 2020.
[11] R. Rabbany, D. Eswaran, A. W. Dubrawski, and C. Faloutsos, “Beyond assortativity: proclivity index for attributed networks (p ro n e),” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, 2017.
[12] K. Pelechrinis and D. Wei, “Va-index: Quantifying assortativity patterns in networks with multidimensional nodal attributes,” PloS one, vol. 11, no. 1, p. e0146188, 2016.
[13] R. Noldus and P. Van Mieghem, “Assortativity in complex networks,” Journal of Complex Networks, vol. 3, no. 4, pp. 507-542, 2015.
[14] A. Allen-Perkins, J. M. Pastor, and E. Estrada, “Two-walks degree assortativity in graphs and networks,” Applied Mathematics and Computation, vol. 311, pp. 262-271, 2017.
[15] S.-C. Ngo, A. G. Percus, K. Burghardt, and K. Lerman, “The transsortative structure of networks,” Proceedings of the Royal Society A, vol. 476, no. 2237, p. 20190772, 2020.
[16] E. Lee, F. Karimi, C. Wagner, H.-H. Jo, M. Strohmaier, and M. Galesic, “Homophily and minority size explain perception biases in social networks,” arXiv preprint arXiv:1710.08601, 2017.
[17] K. M. Altenburger and J. Ugander, “Monophily in social networks introduces similarity among friends-of-friends,” Nature human behaviour, vol. 2, no. 4, pp. 284-290, 2018.
[18] G. T. Cantwell and M. Newman, “Mixing patterns and individual differences in networks,” Physical Review E, vol. 99, no. 4, p. 042306, 2019.
[19] L. Gutiérrez-Gómez and J.-C. Delvenne, “Multi-hop assortativities for network classification,” Journal of Complex Networks, vol. 7, no. 4, pp. 603-622, 2019.
[20] W. W. Zachary, “An information flow model for conflict and fission in small groups,” Journal of anthropological research, 1977.
[21] L. Peel, D. B. Larremore, and A. Clauset, “The ground truth about metadata and community detection in networks,” Science advances, vol. 3, no. 5, p. e1602548, 2017.
[22] P. Sapiezynski, A. Stopczynski, D. D. Lassen, and S. Lehmann, “Interaction data from the copenhagen networks study,” Scientific Data, vol. 6, no. 1, pp. 1-10, 2019.
[23] A. L. Traud, P. J. Mucha, and M. A. Porter, “Social structure of facebook networks,” CoRR, vol. abs/1102.2166, 2011.
[24] M. Cinelli, L. Peel, A. Iovanella, and J.-C. Delvenne, “Network constraints on the mixing patterns of binary node metadata,” arXiv preprint arXiv:1908.04588, 2019.
[25] S. Citraro and G. Rossetti, “Identifying and exploiting homogeneous communities in labeled networks,” Applied Network Science, vol. 5, no. 1, pp. 1-20, 2020.
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@article{oai:it.cnr:prodotti:454273,
title = {Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks},
author = {Rossetti G. and Citraro S. and Milli L.},
publisher = {IEEE Computer Society,, Los Alamitos, CA , Stati Uniti d'America},
doi = {10.1109/mis.2021.3051291 and 10.48550/arxiv.2012.05195},
journal = {IEEE intelligent systems},
volume = {36},
pages = {25–34},
year = {2021}
}
0000-0002-5021-4790ISTI Repository
DOI
10.1109/mis.2021.3051291
10.48550/arxiv.2012.05195
arXiv.org e-Print Archive
ieeexplore.ieee.org
IEEE Intelligent Systems
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SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
