2022
Journal article
Open Access
Delta-Conformity: multi-scale node assortativity in feature-rich stream graphs
Citraro S, Milli L, Cazabet R, Rossetti G
Computer Science - Social and Information Networks [INFO]Computer Science [cs] Dynamic networks 004 Homophily Stream graphs Mixing patterns
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.
Source: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS, vol. 17, pp. 153-164
1. Kristen M Altenburger and Johan Ugander. Monophily in social networks introduces similarity among friends-of-friends. Nature human behaviour, 2(4):284-290, 2018.
2. Sevgi O Aral, James P Hughes, Bradley Stoner, William Whittington, H Hunter Handsfield, Roy M Anderson, and King K Holmes. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. American Journal of Public Health, 89(6):825-833, 1999.
3. Mauro Barone and Michele Coscia. Birds of a feather scam together: Trustworthiness homophily in a business network. Social Networks, 54:228-237, 2018.
4. Aleix Bassolas and Vincenzo Nicosia. First-passage times to quantify and compare structural correlations and heterogeneity in complex systems. Communications Physics, 4(1):1-14, 2021.
5. George T Cantwell and MEJ Newman. Mixing patterns and individual differences in networks. Physical Review E, 99(4):042306, 2019.
6. Laetitia Gauvin, Mathieu Génois, Márton Karsai, Mikko Kivelä, Taro Takaguchi, Eugenio Valdano, and Christian L Vestergaard. Randomized reference models for temporal networks. arXiv preprint arXiv:1806.04032, 2018.
7. Leonardo Gutiérrez-Gómez and Jean-Charles Delvenne. Multi-hop assortativities for network classification. Journal of Complex Networks, 7(4):603-622, 2019.
8. Martin Harrigan and Christoph Fretter. The unreasonable effectiveness of address clustering. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pages 368-373. IEEE, 2016.
9. Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron, and Alessandra Sala. Feature-rich networks: going beyond complex network topologies. Applied Network Science, 4(1):1-13, 2019.
10. Simmi Marina Joseph, Salvatore Citraro, Virginia Morini, Giulio Rossetti, and Massimo Stella. Cognitive network science quantifies feelings expressed in suicide letters and reddit mental health communities. arXiv preprint arXiv:2110.15269, 2021.
11. Marc Jourdan, Sebastien Blandin, Laura Wynter, and Pralhad Deshpande. Characterizing entities in the bitcoin blockchain. In 2018 IEEE international conference on data mining workshops (ICDMW), pages 55-62. IEEE, 2018.
12. Dániel Kondor, Márton Pósfai, István Csabai, and Gábor Vattay. Do the rich get richer? an empirical analysis of the bitcoin transaction network. PloS one, 9(2):e86197, 2014.
13. Lauri Kovanen, Kimmo Kaski, János Kertész, and Jari Saramäki. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences, 110(45):18070-18075, 2013.
14. Matthieu Latapy, Tiphaine Viard, and Clémence Magnien. Stream graphs and link streams for the modeling of interactions over time. Social Network Analysis and Mining, 8(1):1-29, 2018.
15. Eun Lee, Fariba Karimi, Claudia Wagner, Hang-Hyun Jo, Markus Strohmaier, and Mirta Galesic. Homophily and minority-group size explain perception biases in social networks. Nature human behaviour, 3(10):1078-1087, 2019.
16. Rossana Mastrandrea, Julie Fournet, and Alain Barrat. Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PloS one, 10(9):e0136497, 2015.
17. Miller McPherson, Lynn Smith-Lovin, and James M Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, 2001.
18. Michael Molloy, Bruce Reed, Mark Newman, Albert-László Barabási, and Duncan J Watts. A critical point for random graphs with a given degree sequence. In The Structure and Dynamics of Networks, pages 240-258. Princeton University Press, 2011.
19. James Moody. Race, school integration, and friendship segregation in america. American journal of Sociology, 107(3):679-716, 2001.
20. Virginia Morini, Laura Pollacci, and Giulio Rossetti. Toward a standard approach for echo chamber detection: Reddit case study. Applied Sciences, 11(12):5390, 2021.
21. Mark EJ Newman. Mixing patterns in networks. Physical review E, 67(2):026126, 2003.
22. Pimprenelle Parmentier, Tiphaine Viard, Benjamin Renoust, and J-F Baffier. Introducing multilayer stream graphs and layer centralities. In International Conference on Complex Networks and Their Applications, pages 684-696. Springer, 2019.
23. Leto Peel, Jean-Charles Delvenne, and Renaud Lambiotte. Multiscale mixing patterns in networks. Proceedings of the National Academy of Sciences, 115(16):4057-4062, 2018.
24. Konstantinos Pelechrinis and Dong Wei. Va-index: Quantifying assortativity patterns in networks with multidimensional nodal attributes. PloS one, 11(1):e0146188, 2016.
25. Reihaneh Rabbany, Dhivya Eswaran, Artur W Dubrawski, and Christos Faloutsos. Beyond assortativity: proclivity index for attributed networks (p ro n e). In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 2017.
26. Amit Singh Rathore, Mandar R Mutalikdesai, and Sanket Patil. Analyzing trust-based mixing patterns in signed networks. In International Conference on Asian Digital Libraries, pages 63-72. Springer, 2013.
27. Cazabet Remy, Baccour Rym, and Latapy Matthieu. Tracking bitcoin users activity using community detection on a network of weak signals. In International conference on complex networks and their applications, pages 166-177. Springer, 2017.
28. Giulio Rossetti, Salvatore Citraro, and Letizia Milli. Conformity: a path-aware homophily measure for node-attributed networks. IEEE Intelligent Systems, 2021.
29. Piotr Sapiezynski, Arkadiusz Stopczynski, David Dreyer Lassen, and Sune Lehmann. Interaction data from the copenhagen networks study. Scientific Data, 6(1):1-10, 2019.
30. Brandon Sepulvado, Michael Lee Wood, Ethan Fridmanski, Cheng Wang, Matthew J Chandler, Omar Lizardo, and David Hachen. Predicting homophily and social network connectivity from dyadic behavioral similarity trajectory clusters. Social Science Computer Review, page 0894439320923123, 2020.
31. Wesley Shrum, Neil H Cheek Jr, and Saundra MacD. Friendship in school: Gender and racial homophily. Sociology of Education, pages 227-239, 1988.
32. Frédéric Simard. On computing distances and latencies in link streams. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 394-397. IEEE, 2019.
33. Frédéric Simard. Evaluating metrics in link streams. Social Network Analysis and Mining, 11(1):1-16, 2021.
34. Frédéric Simard, Clémence Magnien, and Matthieu Latapy. Computing betweenness centrality in link streams. arXiv preprint arXiv:2102.06543, 2021.
35. Juliette Stehlé, Nicolas Voirin, Alain Barrat, Ciro Cattuto, Lorenzo Isella, Jean-François Pinton, Marco Quaggiotto, Wouter Van den Broeck, Corinne Régis, Bruno Lina, et al. High-resolution measurements of face-to-face contact patterns in a primary school. PloS one, 6(8):e23176, 2011.
36. Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-François Pinton, Nagham Khanafer, Corinne Régis, Byeul-a Kim, Brigitte Comte, and Nicolas Voirin. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS one, 8(9):e73970, 2013.
37. Bin Zhou, Xin Lu, and Petter Holme. Universal evolution patterns of degree assortativity in social networks. Social Networks, 63:47-55, 2020.
1. Altenburger, K.M., Ugander, J.: Monophily in social networks introduces similarity among friends-of-friends. Nat. Hum. Behav. 2(4), 284-290 (2018)
2. Aral, S.O., Hughes, J.P., Stoner, B., Whittington, W., Handsfield, H.H., Anderson, R.M., Holmes, K.K.: Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am. J. Public Health 89(6), 825-833 (1999)
3. Barone, M., Coscia, M.: Birds of a feather scam together: trustworthiness homophily in a business network. Soc. Netw. 54, 228-237 (2018)
4. Bassolas, A., Nicosia, V.: First-passage times to quantify and compare structural correlations and heterogeneity in complex systems. Commun. Phys. 4(1), 1-14 (2021)
5. Cantwell, G.T., Newman, M.E.J.: Mixing patterns and individual differences in networks. Phys. Rev. E 99(4), 042306 (2019)
6. Coscia, M.: The atlas for the aspiring network scientist. arXiv preprint arXiv:2101.00863 (2021)
7. Gao, J., Zhang, Y.-C., Zhou, T.: Computational socioeconomics. Phys. Rep. 817, 1-104 (2019)
8. Gauvin, L., Génois, M., Karsai, M., Kivelä, M., Takaguchi, T., Valdano, E., Vestergaard, C.L.: Randomized reference models for temporal networks. arXiv preprint arXiv:1806.04032 (2018)
9. Gutiérrez-Gómez, L., Delvenne, J.-C.: Multi-hop assortativities for network classification. J. Complex Netw. 7(4), 603-622 (2019)
10. Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 368-373. IEEE (2016)
11. Interdonato, R., Atzmueller, M., Gaito, S., Kanawati, R., Largeron, C., Sala, A.: Feature-rich networks: going beyond complex network topologies. Appl. Netw. Sci. 4(1), 1-13 (2019)
12. Joseph, S.M., Citraro, S., Morini, V., Rossetti, G., Stella, M.: Cognitive network science quantifies feelings expressed in suicide letters and reddit mental health communities. arXiv preprint arXiv:2110.15269 (2021)
13. Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 55-62. IEEE (2018)
14. Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? An empirical analysis of the bitcoin transaction network. PLoS ONE 9(2), e86197 (2014)
15. Kovanen, L., Kaski, K., Kertész, J., Saramäki, J.: Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proc. Natl. Acad. Sci. 110(45), 18070-18075 (2013)
16. Latapy, M., Viard, T., Magnien, C.: Stream graphs and link streams for the modeling of interactions over time. Soc. Netw. Anal. Min. 8(1), 1-29 (2018)
17. Lee, E., Karimi, F., Wagner, C., Jo, H.-H., Strohmaier, M., Galesic, M.: Homophily and minority-group size explain perception biases in social networks. Nat. Hum. Behav. 3(10), 1078-1087 (2019)
18. Mastrandrea, R., Fournet, J., Barrat, A.: Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE 10(9), e0136497 (2015)
19. McPherson, M., Smith-Lovin, L., Cook, J.M.: Homophily in social networks. Annual review of sociology, Birds of a feather (2001)
20. Molloy, M., Reed, B., Newman, M., Barabási, A.-L., Watts, D.J.: A critical point for random graphs with a given degree sequence. In: The Structure and Dynamics of Networks, pp. 240-258. Princeton University Press (2011)
21. Moody, J.: Race, school integration, and friendship segregation in America. Am. J. Sociol. 107(3), 679-716 (2001)
22. Morini, V., Pollacci, L., Rossetti, G.: Toward a standard approach for echo chamber detection: reddit case study. Appl. Sci. 11(12), 5390 (2021)
23. Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 026126 (2003)
24. Parmentier, P., Viard, T., Renoust, B., Baffier, J.-F.: Introducing multilayer stream graphs and layer centralities. In: International Conference on Complex Networks and Their Applications, pp. 684-696. Springer (2019)
25. Peel, L., Delvenne, J.-C., Lambiotte, R.: Multiscale mixing patterns in networks. Proc. Natl. Acad. Sci. 115(16), 4057-4062 (2018)
26. Pelechrinis, K., Wei, D.: VA-index: quantifying assortativity patterns in networks with multidimensional nodal attributes. PLoS ONE 11(1), e0146188 (2016)
27. Posfai, M., Barabási, A.-L.: Network Science. Cambridge University Press, Cambridge (2016)
28. Rabbany, R., Eswaran, D., Dubrawski, A.W., Faloutsos, C.: Beyond assortativity: proclivity index for attributed networks (PRONE). In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer (2017)
29. Rathore, A.S., Mutalikdesai, M.R., Patil, S.: Analyzing trust-based mixing patterns in signed networks. In: International Conference on Asian Digital Libraries, pp. 63-72. Springer (2013)
30. Remy, C., Rym, B., Matthieu, L.: Tracking bitcoin users activity using community detection on a network of weak signals. In: International Conference on Complex Networks and Their Applications, pp. 166-177. Springer (2017)
31. Rossetti, G., Citraro, S., Milli, L.: Conformity: a path-aware homophily measure for node-attributed networks. IEEE Intell. Syst. 36, 25-34 (2021)
32. Sapiezynski, P., Stopczynski, A., Lassen, D.D., Lehmann, S.: Interaction data from the Copenhagen networks study. Sci. Data 6(1), 1-10 (2019)
33. Sepulvado, B., Wood, M.L., Fridmanski, E., Wang, C., Chandler, M.J., Lizardo, O., Hachen, D.: Predicting homophily and social network connectivity from dyadic behavioral similarity trajectory clusters. Soc. Sci. Comput. Rev. 0894439320923123 (2020)
34. Shrum, W., Cheek Jr, N.H., MacD, S.: Friendship in school: gender and racial homophily. Sociol. Educ. 227-239 (1988)
35. Simard, F.: On computing distances and latencies in link streams. In: 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 394-397. IEEE (2019)
36. Simard, F.: Evaluating metrics in link streams. Soc. Netw. Anal. Min. 11(1), 1-16 (2021)
37. Simard, F., Magnien, C., Latapy, M.: Computing betweenness centrality in link streams. arXiv preprint arXiv:2102.06543 (2021)
38. Stehlé, J., Voirin, N., Barrat, A., Cattuto, C., Isella, L., Pinton, J.- F., Quaggiotto, M., Van den Broeck, W., Régis, C., Lina, B., et al.: High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE 6(8), e23176 (2011)
39. Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., Régis, C., Kim, B., Comte, B., Voirin, N.: Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS ONE 8(9), e73970 (2013)
40. Zhou, B., Xin, L., Holme, P.: Universal evolution patterns of degree assortativity in social networks. Soc. Netw. 63, 47-55 (2020)
2. Sevgi O Aral, James P Hughes, Bradley Stoner, William Whittington, H Hunter Handsfield, Roy M Anderson, and King K Holmes. Sexual mixing patterns in the spread of gonococcal and chlamydial infections. American Journal of Public Health, 89(6):825-833, 1999.
3. Mauro Barone and Michele Coscia. Birds of a feather scam together: Trustworthiness homophily in a business network. Social Networks, 54:228-237, 2018.
4. Aleix Bassolas and Vincenzo Nicosia. First-passage times to quantify and compare structural correlations and heterogeneity in complex systems. Communications Physics, 4(1):1-14, 2021.
5. George T Cantwell and MEJ Newman. Mixing patterns and individual differences in networks. Physical Review E, 99(4):042306, 2019.
6. Laetitia Gauvin, Mathieu Génois, Márton Karsai, Mikko Kivelä, Taro Takaguchi, Eugenio Valdano, and Christian L Vestergaard. Randomized reference models for temporal networks. arXiv preprint arXiv:1806.04032, 2018.
7. Leonardo Gutiérrez-Gómez and Jean-Charles Delvenne. Multi-hop assortativities for network classification. Journal of Complex Networks, 7(4):603-622, 2019.
8. Martin Harrigan and Christoph Fretter. The unreasonable effectiveness of address clustering. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pages 368-373. IEEE, 2016.
9. Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati, Christine Largeron, and Alessandra Sala. Feature-rich networks: going beyond complex network topologies. Applied Network Science, 4(1):1-13, 2019.
10. Simmi Marina Joseph, Salvatore Citraro, Virginia Morini, Giulio Rossetti, and Massimo Stella. Cognitive network science quantifies feelings expressed in suicide letters and reddit mental health communities. arXiv preprint arXiv:2110.15269, 2021.
11. Marc Jourdan, Sebastien Blandin, Laura Wynter, and Pralhad Deshpande. Characterizing entities in the bitcoin blockchain. In 2018 IEEE international conference on data mining workshops (ICDMW), pages 55-62. IEEE, 2018.
12. Dániel Kondor, Márton Pósfai, István Csabai, and Gábor Vattay. Do the rich get richer? an empirical analysis of the bitcoin transaction network. PloS one, 9(2):e86197, 2014.
13. Lauri Kovanen, Kimmo Kaski, János Kertész, and Jari Saramäki. Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proceedings of the National Academy of Sciences, 110(45):18070-18075, 2013.
14. Matthieu Latapy, Tiphaine Viard, and Clémence Magnien. Stream graphs and link streams for the modeling of interactions over time. Social Network Analysis and Mining, 8(1):1-29, 2018.
15. Eun Lee, Fariba Karimi, Claudia Wagner, Hang-Hyun Jo, Markus Strohmaier, and Mirta Galesic. Homophily and minority-group size explain perception biases in social networks. Nature human behaviour, 3(10):1078-1087, 2019.
16. Rossana Mastrandrea, Julie Fournet, and Alain Barrat. Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PloS one, 10(9):e0136497, 2015.
17. Miller McPherson, Lynn Smith-Lovin, and James M Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, 2001.
18. Michael Molloy, Bruce Reed, Mark Newman, Albert-László Barabási, and Duncan J Watts. A critical point for random graphs with a given degree sequence. In The Structure and Dynamics of Networks, pages 240-258. Princeton University Press, 2011.
19. James Moody. Race, school integration, and friendship segregation in america. American journal of Sociology, 107(3):679-716, 2001.
20. Virginia Morini, Laura Pollacci, and Giulio Rossetti. Toward a standard approach for echo chamber detection: Reddit case study. Applied Sciences, 11(12):5390, 2021.
21. Mark EJ Newman. Mixing patterns in networks. Physical review E, 67(2):026126, 2003.
22. Pimprenelle Parmentier, Tiphaine Viard, Benjamin Renoust, and J-F Baffier. Introducing multilayer stream graphs and layer centralities. In International Conference on Complex Networks and Their Applications, pages 684-696. Springer, 2019.
23. Leto Peel, Jean-Charles Delvenne, and Renaud Lambiotte. Multiscale mixing patterns in networks. Proceedings of the National Academy of Sciences, 115(16):4057-4062, 2018.
24. Konstantinos Pelechrinis and Dong Wei. Va-index: Quantifying assortativity patterns in networks with multidimensional nodal attributes. PloS one, 11(1):e0146188, 2016.
25. Reihaneh Rabbany, Dhivya Eswaran, Artur W Dubrawski, and Christos Faloutsos. Beyond assortativity: proclivity index for attributed networks (p ro n e). In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 2017.
26. Amit Singh Rathore, Mandar R Mutalikdesai, and Sanket Patil. Analyzing trust-based mixing patterns in signed networks. In International Conference on Asian Digital Libraries, pages 63-72. Springer, 2013.
27. Cazabet Remy, Baccour Rym, and Latapy Matthieu. Tracking bitcoin users activity using community detection on a network of weak signals. In International conference on complex networks and their applications, pages 166-177. Springer, 2017.
28. Giulio Rossetti, Salvatore Citraro, and Letizia Milli. Conformity: a path-aware homophily measure for node-attributed networks. IEEE Intelligent Systems, 2021.
29. Piotr Sapiezynski, Arkadiusz Stopczynski, David Dreyer Lassen, and Sune Lehmann. Interaction data from the copenhagen networks study. Scientific Data, 6(1):1-10, 2019.
30. Brandon Sepulvado, Michael Lee Wood, Ethan Fridmanski, Cheng Wang, Matthew J Chandler, Omar Lizardo, and David Hachen. Predicting homophily and social network connectivity from dyadic behavioral similarity trajectory clusters. Social Science Computer Review, page 0894439320923123, 2020.
31. Wesley Shrum, Neil H Cheek Jr, and Saundra MacD. Friendship in school: Gender and racial homophily. Sociology of Education, pages 227-239, 1988.
32. Frédéric Simard. On computing distances and latencies in link streams. In 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 394-397. IEEE, 2019.
33. Frédéric Simard. Evaluating metrics in link streams. Social Network Analysis and Mining, 11(1):1-16, 2021.
34. Frédéric Simard, Clémence Magnien, and Matthieu Latapy. Computing betweenness centrality in link streams. arXiv preprint arXiv:2102.06543, 2021.
35. Juliette Stehlé, Nicolas Voirin, Alain Barrat, Ciro Cattuto, Lorenzo Isella, Jean-François Pinton, Marco Quaggiotto, Wouter Van den Broeck, Corinne Régis, Bruno Lina, et al. High-resolution measurements of face-to-face contact patterns in a primary school. PloS one, 6(8):e23176, 2011.
36. Philippe Vanhems, Alain Barrat, Ciro Cattuto, Jean-François Pinton, Nagham Khanafer, Corinne Régis, Byeul-a Kim, Brigitte Comte, and Nicolas Voirin. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS one, 8(9):e73970, 2013.
37. Bin Zhou, Xin Lu, and Petter Holme. Universal evolution patterns of degree assortativity in social networks. Social Networks, 63:47-55, 2020.
1. Altenburger, K.M., Ugander, J.: Monophily in social networks introduces similarity among friends-of-friends. Nat. Hum. Behav. 2(4), 284-290 (2018)
2. Aral, S.O., Hughes, J.P., Stoner, B., Whittington, W., Handsfield, H.H., Anderson, R.M., Holmes, K.K.: Sexual mixing patterns in the spread of gonococcal and chlamydial infections. Am. J. Public Health 89(6), 825-833 (1999)
3. Barone, M., Coscia, M.: Birds of a feather scam together: trustworthiness homophily in a business network. Soc. Netw. 54, 228-237 (2018)
4. Bassolas, A., Nicosia, V.: First-passage times to quantify and compare structural correlations and heterogeneity in complex systems. Commun. Phys. 4(1), 1-14 (2021)
5. Cantwell, G.T., Newman, M.E.J.: Mixing patterns and individual differences in networks. Phys. Rev. E 99(4), 042306 (2019)
6. Coscia, M.: The atlas for the aspiring network scientist. arXiv preprint arXiv:2101.00863 (2021)
7. Gao, J., Zhang, Y.-C., Zhou, T.: Computational socioeconomics. Phys. Rep. 817, 1-104 (2019)
8. Gauvin, L., Génois, M., Karsai, M., Kivelä, M., Takaguchi, T., Valdano, E., Vestergaard, C.L.: Randomized reference models for temporal networks. arXiv preprint arXiv:1806.04032 (2018)
9. Gutiérrez-Gómez, L., Delvenne, J.-C.: Multi-hop assortativities for network classification. J. Complex Netw. 7(4), 603-622 (2019)
10. Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: 2016 International IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pp. 368-373. IEEE (2016)
11. Interdonato, R., Atzmueller, M., Gaito, S., Kanawati, R., Largeron, C., Sala, A.: Feature-rich networks: going beyond complex network topologies. Appl. Netw. Sci. 4(1), 1-13 (2019)
12. Joseph, S.M., Citraro, S., Morini, V., Rossetti, G., Stella, M.: Cognitive network science quantifies feelings expressed in suicide letters and reddit mental health communities. arXiv preprint arXiv:2110.15269 (2021)
13. Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 55-62. IEEE (2018)
14. Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? An empirical analysis of the bitcoin transaction network. PLoS ONE 9(2), e86197 (2014)
15. Kovanen, L., Kaski, K., Kertész, J., Saramäki, J.: Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences. Proc. Natl. Acad. Sci. 110(45), 18070-18075 (2013)
16. Latapy, M., Viard, T., Magnien, C.: Stream graphs and link streams for the modeling of interactions over time. Soc. Netw. Anal. Min. 8(1), 1-29 (2018)
17. Lee, E., Karimi, F., Wagner, C., Jo, H.-H., Strohmaier, M., Galesic, M.: Homophily and minority-group size explain perception biases in social networks. Nat. Hum. Behav. 3(10), 1078-1087 (2019)
18. Mastrandrea, R., Fournet, J., Barrat, A.: Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE 10(9), e0136497 (2015)
19. McPherson, M., Smith-Lovin, L., Cook, J.M.: Homophily in social networks. Annual review of sociology, Birds of a feather (2001)
20. Molloy, M., Reed, B., Newman, M., Barabási, A.-L., Watts, D.J.: A critical point for random graphs with a given degree sequence. In: The Structure and Dynamics of Networks, pp. 240-258. Princeton University Press (2011)
21. Moody, J.: Race, school integration, and friendship segregation in America. Am. J. Sociol. 107(3), 679-716 (2001)
22. Morini, V., Pollacci, L., Rossetti, G.: Toward a standard approach for echo chamber detection: reddit case study. Appl. Sci. 11(12), 5390 (2021)
23. Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E 67(2), 026126 (2003)
24. Parmentier, P., Viard, T., Renoust, B., Baffier, J.-F.: Introducing multilayer stream graphs and layer centralities. In: International Conference on Complex Networks and Their Applications, pp. 684-696. Springer (2019)
25. Peel, L., Delvenne, J.-C., Lambiotte, R.: Multiscale mixing patterns in networks. Proc. Natl. Acad. Sci. 115(16), 4057-4062 (2018)
26. Pelechrinis, K., Wei, D.: VA-index: quantifying assortativity patterns in networks with multidimensional nodal attributes. PLoS ONE 11(1), e0146188 (2016)
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BibTeX entry
@article{oai:it.cnr:prodotti:477661,
title = {Delta-Conformity: multi-scale node assortativity in feature-rich stream graphs},
author = {Citraro S and Milli L and Cazabet R and Rossetti G},
doi = {10.1007/s41060-022-00375-4},
year = {2022}
}
0000-0002-5021-4790CNR IRIS
ISTI Repository
DOIAlso available from
ISTI Repository
DOIAlso available from
arXiv.org e-Print Archive
IRIS Cnr
IRIS Cnr
HAL-ENS-LYON
Archivio della Ricerca - Università di Pisa
BITUNAM
Bitcoin User Network Analysis and Mining
SAI: Social Explainable Artificial Intelligence
SoBigData
SoBigData Research Infrastructure
SoBigData-PlusPlus
SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics
Social Explainable Artificial Intelligence (SAI)
