- Letter
- Open Access
Assessing frustration in real-world signed networks: A statistical theory of balance
Phys. Rev. Research 6, L042065 – Published 19 December, 2024
DOI: https://doi.org/10.1103/PhysRevResearch.6.L042065
Abstract
According to the so-called strong version of structural balance theory, actors in signed social networks avoid establishing triads with an odd number of negative links. Generalizing, the weak version of balance theory allows for nodes to be partitioned into any number of blocks with positive internal links, mutually connected by negative links. If this prescription is interpreted rigidly, i.e., without allowing for statistical noise in the observed link signs, then most real graphs will appear to require a larger number of blocks than the actual one, or even to violate both versions of the theory. This might lead to conclusions invoking even more relaxed notions of balance. Here, after rephrasing structural balance theory in statistically testable terms, we propose an inference scheme to unambiguously assess whether a real-world signed graph is balanced. We find that the proposed statistical balance theory leads to interpretations that are quite different from those derived from the current deterministic versions of the theory.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (32)
- F. Heider, Attitudes and cognitive organization, J. Psychol. 21, 107 (1946).
- D. Cartwright and F. Harary, Structural balance: A generalization of Heider's theory, Psychol. Rev. 63, 277 (1956).
- F. Harary, M.-H. Lim, and D. C. Wunsch, Signed graphs for portfolio analysis in risk management, IMA J. Manag. Math. 13, 201 (2002).
- L. Ou-Yang, D.-Q. Dai, and X.-F. Zhang, Detecting protein complexes from signed protein-protein interaction networks, IEEE/ACM Trans. Comput. Biol. Bioinform. 12, 1333 (2015).
- F. Iorio, M. Bernardo-Faura, A. Gobbi, T. Cokelaer, G. Jurman, and J. Saez-Rodriguez, Efficient randomization of biological networks while preserving functional characterization of individual nodes, BMC Bioinf. 17, 542 (2016).
- H. Saiz, J. Gómez-Gardeñes, P. Nuche, A. Girón, Y. Pueyo, and C. L. Alados, Evidence of structural balance in spatial ecological networks, Ecography 40, 733 (2017).
- J. A. Davis, Clustering and structural balance in graphs, Hum. Relat. 20, 181 (1967).
- P. Doreian and A. Mrvar, A partitioning approach to structural balance, Soc. Netw. 18, 149 (1996).
- P. Anchuri and M. Magdon-Ismail, Communities and balance in signed networks: A spectral approach, in Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (IEEE Computer Society, Massachusetts, NW Washington, DC, USA, 2012), pp. 235–242.
- P. Doreian and A. Mrvar, Partitioning signed social networks, Soc. Netw. 31, 1 (2009).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.6.L042065 for more details about the employed formalism, the derivation of the main results of the paper and additional exercises.
- F. Harary, On the measurement of structural balance, Behav. Sci. 4, 316 (1959).
- V. Traag, P. Doreian, and A. Mrvar, Partitioning signed networks, in Advances in Network Clustering and Blockmodeling (Wiley, Hoboken, NJ, 2019), Chap. 8, pp. 225–249.
- F. Harary, On the notion of balance of a signed graph, Mich. Math. J. 2, 143 (1953).
- T. Zasĺavsky, Balanced decompositions of a signed graph, J. Comb. Theory, Ser. B 43, 1 (1987).
- S. Aref and M. C. Wilson, Balance and frustration in signed networks, J. Complex Netw. 7, 163 (2019).
- M. Ruiz-García, J. Ozaita, M. Pereda, A. Alfonso, P. Brañas-Garza, J. A. Cuesta, and A. Sánchez, Triadic influence as a proxy for compatibility in social relationships, Proc. Natl. Acad. Sci. USA 120, e2215041120 (2023).
- P. Doreian and A. Mrvar, Structural balance and signed international relations, J. Soc. Struct. 16, 1 (2015).
- S. Aref, L. Dinh, R. Rezapour, and J. Diesner, Multilevel structural evaluation of signed directed social networks based on balance theory, Sci. Rep. 10, 15228 (2020).
- V. A. Traag and J. Bruggeman, Community detection in networks with positive and negative links, Phys. Rev. E 80, 036115 (2009).
- B. Yang, X. Zhao, and X. Liu, Bayesian approach to modeling and detecting communities in signed network, in Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI Press, Washington, DC, 2015).
- J. Q. Jiang, Stochastic block model and exploratory analysis in signed networks, Phys. Rev. E 91, 062805 (2015).
- B. Yang, X. Liu, Y. Li, and X. Zhao, Stochastic blockmodeling and variational Bayes learning for signed network analysis, IEEE Trans. Knowl. Data Eng. 29, 2026 (2017).
- A. E. Raftery, Bayesian model selection in social research, Sociol. Methodol. 25, 111 (1995).
- S. Konishi and G. Kitagawa, Information Criteria and Statistical Modeling (Springer, Berlin, 2008).
- S. Gómez, P. Jensen, and A. Arenas, Analysis of community structure in networks of correlated data, Phys. Rev. E 80, 016114 (2009).
- A. Amelio and C. Pizzuti, Community mining in signed networks: A multiobjective approach, in Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Association for Computing Machinery, New York, 2013), pp. 95–99.
- P. Esmailian and M. Jalili, Community detection in signed networks: The role of negative ties in different scales, Sci. Rep. 5, 14339 (2015).
- Y. Su, B. Wang, F. Cheng, L. Zhang, X. Zhang, and L. Pan, An algorithm based on positive and negative links for community detection in signed networks, Sci. Rep. 7, 10874 (2017).
- A. Gallo, D. Garlaschelli, R. Lambiotte, F. Saracco, and T. Squartini, Testing structural balance theories in heterogeneous signed networks, Commun. Phys. 7, 154 (2024).
- B. Hao and I. A. Kovács, Proper network randomization is key to assessing social balance, Sci. Adv. 10, eadj0104 (2024).
- A. Gallo, F. Saracco, R. Lambiotte, D. Garlaschelli, and T. Squartini, Patterns of link reciprocity in directed, signed networks, arXiv:2407.08697.