Interplay between tie strength and neighbourhood topology in complex networks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 Apr 2024
03 Apr 2024
Historique:
received:
12
02
2024
accepted:
28
03
2024
medline:
3
4
2024
pubmed:
3
4
2024
entrez:
2
4
2024
Statut:
epublish
Résumé
Granovetter's weak ties theory is a very important sociological theory according to which a correlation between edge weight and the network's topology should exist. More specifically, the neighbourhood overlap of two nodes connected by an edge should be positively correlated with edge weight (tie strength). However, some real social networks exhibit a negative correlation-the most prominent example is the scientific collaboration network, for which overlap decreases with edge weight. It has been demonstrated that the aforementioned inconsistency with Granovetter's theory can be alleviated in the scientific collaboration network through the use of asymmetric measures. In this paper, we explain that while asymmetric measures are often necessary to describe complex networks and to confirm Granovetter's theory, their interpretation is not simple, and there are pitfalls that one must be wary of. The definitions of asymmetric weights and overlaps introduce structural correlations that must be filtered out. We show that correlation profiles can be used to overcome this problem. Using this technique, not only do we confirm Granovetter's theory in various real and artificial social networks, but we also show that Granovetter-like weight-topology correlations are present in other complex networks (e.g. metabolic and neural networks). Our results suggest that Granovetter's theory is a sociological manifestation of more general principles governing various types of complex networks.
Identifiants
pubmed: 38565614
doi: 10.1038/s41598-024-58357-4
pii: 10.1038/s41598-024-58357-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7811Informations de copyright
© 2024. The Author(s).
Références
Granovetter, M. S. The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973).
doi: 10.1086/225469
Granovetter, M. S. Getting A Job: A Study of Contacts and Careers (University of Chicago Press, 2018).
Onnela, J.-P. et al. Structure and tie strengths in mobile communication networks. PNAS 104, 7332–7336 (2007).
doi: 10.1073/pnas.0610245104
pubmed: 17456605
pmcid: 1863470
Easley, D. & Kleinberg, J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World (Cambridge University Press, 2010).
doi: 10.1017/CBO9780511761942
Eagle, N., Macy, M. & Claxton, R. Network diversity and economic development. Science 328, 1029–1031 (2010).
doi: 10.1126/science.1186605
pubmed: 20489022
Pajevic, S. & Plenz, D. The organization of strong links in complex networks. Nat. Phys. 8, 429–436 (2012).
doi: 10.1038/nphys2257
pubmed: 28890731
pmcid: 5589347
Grabowicz, P. A., Ramasco, J. J., Moro, E., Pujol, J. M. & Eguiluz, V. M. Social features of online networks: The strength of intermediary ties in online social media. PLoS One 7, e29358 (2012).
doi: 10.1371/journal.pone.0029358
pubmed: 22247773
pmcid: 3256152
Szell, M. & Thurner, S. Measuring social dynamics in a massive multiplayer online game. Soc. Netw. 32, 313–329 (2010).
doi: 10.1016/j.socnet.2010.06.001
Szell, M. & Thurner, S. Social dynamics in a large-scale online game. Adv. Complex Syst. 15, 1250064 (2012).
doi: 10.1142/S0219525912500646
Šuvakov, M., Mitrović, M., Gligorijević, V. & Tadić, B. How the online social networks are used: Dialogues-based structure of myspace. J. R. Soc. Interface 10, 20120819 (2013).
doi: 10.1098/rsif.2012.0819
pubmed: 23193108
pmcid: 3565699
Ke, Q. & Ahn, Y.-Y. Tie strength distribution in scientific collaboration networks. Phys. Rev. E 90, 032804 (2014).
doi: 10.1103/PhysRevE.90.032804
Ubaldi, E., Burioni, R., Loreto, V. & Tria, F. Emergence and evolution of social networks through exploration of the adjacent possible space. Commun. Phys. 4, 28 (2021).
doi: 10.1038/s42005-021-00527-1
Pan, R. K. & Saramäki, J. The strength of strong ties in scientific collaboration networks. Europhys. Lett. 97, 18007 (2012).
doi: 10.1209/0295-5075/97/18007
Fronczak, A., Mrowinski, M. J. & Fronczak, P. Scientific success from the perspective of the strength of weak ties. Sci. Rep. 12, 5074 (2022).
doi: 10.1038/s41598-022-09118-8
pubmed: 35332225
pmcid: 8948253
Newman, M. E. J. Networks: An Introduction (Oxford University Press, 2010).
doi: 10.1093/acprof:oso/9780199206650.001.0001
Dorogovtsev, S. & Mendes, J. The Nature of Complex Networks (Oxford University Press, 2022).
doi: 10.1093/oso/9780199695119.001.0001
Orzechowski, K. P., Mrowinski, M. J., Fronczak, A. & Fronczak, P. Asymmetry of social interactions and its role in link predictability: The case of coauthorship networks. J. Informetr. 17, 101405 (2023).
doi: 10.1016/j.joi.2023.101405
Barrat, A., Barthélemy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. PNAS 101, 3747–3752 (2004).
doi: 10.1073/pnas.0400087101
pubmed: 15007165
pmcid: 374315
Maslov, S. & Sneppen, K. Specificity and stability in topology of protein networks. Science 296, 910–913 (2002).
doi: 10.1126/science.1065103
pubmed: 11988575
Maslov, S., Sneppen, K. & Zaliznyak, A. Detection of topological patterns in complex networks: Correlation profile of the internet. Phys. A 333, 529–540 (2004).
doi: 10.1016/j.physa.2003.06.002
Newman, M. E. J. Assortative mixing in networks. Phys. Rev. Lett. 89, 208701 (2002).
doi: 10.1103/PhysRevLett.89.208701
pubmed: 12443515
Newman, M. E. J. Mixing patterns in networks. Phys. Rev. E 67, 026126 (2003).
doi: 10.1103/PhysRevE.67.026126
Fronczak, A. & Fronczak, P. Networks with given two-point correlations: Hidden correlations from degree correlations. Phys. Rev. E 74, 026121 (2006).
doi: 10.1103/PhysRevE.74.026121
Litvak, N. & van der Hofstad, R. Uncovering disassortativity in large scale-free networks. Phys. Rev. E 87, 022801 (2013).
doi: 10.1103/PhysRevE.87.022801
Tang, J. et al. Arnetminer: Extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’08, 990–998 (Association for Computing Machinery, 2008).
Alberich, R., Miro-Julia, J. & Rossello, F. Marvel universe looks almost like a real social network (2002). arXiv:cond-mat/0202174 .
Li, C. et al. Biomodels database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 4, 92 (2010).
doi: 10.1186/1752-0509-4-92
pubmed: 20587024
pmcid: 2909940
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
doi: 10.1038/30918
pubmed: 9623998
Newman, M. E. J. & Park, J. Why social networks are different from other types of networks. Phys. Rev. E 68, 036122 (2003).
doi: 10.1103/PhysRevE.68.036122
Zhou, T., Ren, J., Medo, M. & Zhang, Y.-C. Bipartite network projection and personal recommendation. Phys. Rev. E 76, 046115 (2007).
doi: 10.1103/PhysRevE.76.046115
Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, J.-P. Understanding tie strength in social networks using a local “bow tie’’ framework. Sci. Rep. 8, 9349 (2018).
doi: 10.1038/s41598-018-27290-8
pubmed: 29921970
pmcid: 6008360
Allard, A., Serrano, M. Á., García-Pérez, G. & Boguñá, M. The geometric nature of weights in real complex networks. Nat. Commun. 8, 14103 (2017).
doi: 10.1038/ncomms14103
pubmed: 28098155
pmcid: 5253659
Serrano, M. A. & Boguñá, M. The Shortest Path to Network Geometry: A Practical Guide to Basic Models and Applications. Elements in the Structure and Dynamics of Complex Networks (Cambridge University Press, 2022).
doi: 10.1017/9781108865791
Boguñá, M. et al. Network geometry. Nat. Rev. Phys. 3, 114–135 (2021).
doi: 10.1038/s42254-020-00264-4
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A. & Boguñá, M. Hyperbolic geometry of complex networks. Phys. Rev. E 82, 036106 (2010).
doi: 10.1103/PhysRevE.82.036106
Budel, G. & Kitsak, M. Complementarity in complex networks (2023). arXiv:2003.06665 .
Kitsak, M., Papadopoulos, F. & Krioukov, D. Latent geometry of bipartite networks. Phys. Rev. E 95, 032309 (2017).
doi: 10.1103/PhysRevE.95.032309
pubmed: 28415237