An economic model of friendship and enmity for measuring social balance in networks

Lee, Kyu-Min; Shin, Euncheol; You, Seungil

doi:10.1016/j.physa.2017.07.012

Cited by 4 publications

(7 citation statements)

References 18 publications

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“…As balance theory introduces correlations between the edge attributes in triads, in a physics framework [14,15,16,17] it maps onto a system with predominant three-edge interactions. Associating the existence of unbalanced triads to the occurrence of a non-vanishing excitation energy, the principles of social balance can be mapped onto a model with variations in an energy landscape.…”

Section: Arxiv:180709042v3 [Physicssoc-ph] 17 Dec 2018mentioning

confidence: 99%

“…Unbalanced [+ + −] and [− − −] triads, however, are a key ingredient in real-life political networks. Balance theory has found applications in many branches of sciences including psychology [1], studies of international networks [3,4,5,6,7], sociology [8,9,10,11,12] and ecology [13].As balance theory introduces correlations between the edge attributes in triads, in a physics framework [14,15,16,17] it maps onto a system with predominant three-edge interactions. Associating the existence of unbalanced triads to the occurrence of a non-vanishing excitation energy, the principles of social balance can be mapped onto a model with variations in an energy landscape.…”

mentioning

confidence: 99%

“…Hence, we face a situation where the time scale of the model and of the data recording is different. As a matter of fact, an extension of the single-edge triad transitions of equation(16) to multiple-edge triad transitions is in order.…”

mentioning

confidence: 99%

“…Left panel: the recorded transitions from April 9 to April 10, 2015. Right panel: the predicted single-edge transitions of equation(16). White sites correspond with M σσ = 0.…”

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confidence: 99%

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Social stability and extended social balance—Quantifying the role of inactive links in social networks

Belaza¹,

Ryckebusch²,

Bramson³

et al. 2019

Physica A: Statistical Mechanics and its Applications

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Structural balance in social network theory starts from signed networks with active relationships (friendly or hostile) to establish a hierarchy between four different types of triadic relationships. The lack of an active link also provides information about the network. To exploit the information that remains uncovered by structural balance, we introduce the inactive relationship that accounts for both neutral and nonexistent ties between two agents. This addition results in ten types of triads, with the advantage that the network analysis can be done with complete networks. To each type of triadic relationship, we assign an energy that is a measure for its average occupation probability. Finite temperatures account for a persistent form of disorder in the formation of the triadic relationships. We propose a Hamiltonian with three interaction terms and a chemical potential (capturing the cost of edge activation) as an underlying model for the triadic energy levels. Our model is suitable for empirical analysis of political networks and allows to uncover generative mechanisms. It is tested on an extended data set for the standings between two classes of alliances in a massively multi-player on-line game (MMOG) and on real-world data for the relationships between countries during the Cold War era. We find emergent properties in the triadic relationships between the nodes in a political network. For example, we observe a persistent hierarchy between the ten triadic energy levels across time and networks. In addition, the analysis reveals consistency in the extracted model parameters and a universal data collapse of a derived combination of global properties of the networks. We illustrate that the model has predictive power for the transition probabilities between the different triadic states. arXiv:1807.09042v3 [physics.soc-ph] 17 Dec 2018Heider [1] and its extension to graphs by Cartwright [2] is based on active relationships that can be friendly ("+") or unfriendly ("−"). The four types of emerging triadic relationships are categorized in two stable (= balanced) and two unstable (= unbalanced) ones, whereby one anticipates an overall tendency to create more balanced triads. Balanced triads [+ + +] and [+ − −] have an even number of "−" edges. Unbalanced [+ + −] and [− − −] triads, however, are a key ingredient in real-life political networks. Balance theory has found applications in many branches of sciences including psychology [1], studies of international networks [3,4,5,6,7], sociology [8,9,10,11,12] and ecology [13].As balance theory introduces correlations between the edge attributes in triads, in a physics framework [14,15,16,17] it maps onto a system with predominant three-edge interactions. Associating the existence of unbalanced triads to the occurrence of a non-vanishing excitation energy, the principles of social balance can be mapped onto a model with variations in an energy landscape. Marvel et al. [14] investigated the energy landscape of a social-balance inspired system and stressed the occurrence ...

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Section: Arxiv:180709042v3 [Physicssoc-ph] 17 Dec 2018mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Left panel: the recorded transitions from April 9 to April 10, 2015. Right panel: the predicted single-edge transitions of equation(16). White sites correspond with M σσ = 0.…”

mentioning

confidence: 99%

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Social stability and extended social balance—Quantifying the role of inactive links in social networks

Belaza¹,

Ryckebusch²,

Bramson³

et al. 2019

Physica A: Statistical Mechanics and its Applications

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“…Modeling the behavior of multi-agent social systems and social networks has recently attracted a substantial amount of activities of researchers, as can be inferred from the review articles [1][2][3] and numerous original papers, of which we can cite just a few recent [4][5][6][7][8]. High interest to such a modeling is due to two reasons.…”

Section: Introduction and Related Literaturementioning

confidence: 99%

Information processing by networks of quantum decision makers

Yukalov

Yukalova

Sornette

2018

Physica A: Statistical Mechanics and its Applications

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We suggest a model of a multi-agent society of decision makers taking decisions being based on two criteria, one is the utility of the prospects and the other is the attractiveness of the considered prospects. The model is the generalization of quantum decision theory, developed earlier for single decision makers realizing one-step decisions, in two principal aspects. First, several decision makers are considered simultaneously, who interact with each other through information exchange. Second, a multistep procedure is treated, when the agents exchange information many times. Several decision makers exchanging information and forming their judgement, using quantum rules, form a kind of a quantum information network, where collective decisions develop in time as a result of information exchange. In addition to characterizing collective decisions that arise in human societies, such networks can describe dynamical processes occurring in artificial quantum intelligence composed of several parts or in a cluster of quantum computers. The practical usage of the theory is illustrated on the dynamic disjunction effect for which three quantitative predictions are made: (i) the probabilistic behavior of decision makers at the initial stage of the process is described; (ii) the decrease of the difference between the initial prospect probabilities and the related utility factors is proved; (iii) the existence of a common consensus after multiple exchange of information is predicted. The predicted numerical values are in very good agreement with empirical data.Keywords: Multi-agent society, artificial quantum intelligence, quantum decision theory, multistep decision procedure, dynamics of collective opinion, quantum information networks, intelligence networks, dynamic disjunction effect * Corresponding author: V.I. Yukalov

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Dyadic imbalance in networks

Burghardt

Maoz

2020

Journal of Complex Networks

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The study of balance in signed networks has been a key topic for over 80 years. Starting with Heider’s theory of structural balance and Harary’s balance theorem, the measurement and understanding of structural balance in networks has grown exponentially. Most theoretical and empirical studies, however, focus on balance on the network as a whole, even though the degree of balance may strongly vary within the network. Our study makes two key contributions to this literature. First, we offer a methodology for measuring balance between nodes in a network. We show that dyadic (im)balance reveals significant information about localized balance that is not apparent in a global metric of balance. Second, we show that this metric can be applied to data containing multiple relations, for example, friendship, enmity, trade and fights, which reveals balance and imbalance otherwise hidden when focusing on only two relations (e.g. friends and enemies). Namely, while balance typically involves unweighted signed networks, multiple relations can naturally be condensed into a weighted signed network where positive (negative) weights correspond to a sum of positive (negative) relations between nodes. Our dyadic imbalance metric can then be applied to this and any other weighted signed network. These findings are illustrated with an international relations network as well as a network on roll call votes in the US Senate and compared to a null model that preserves network structure. We find weak agreement with Heider’s balance theory, including imbalance that decreases in time, and imbalance that is generally lower than our null model.

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An economic model of friendship and enmity for measuring social balance in networks

Cited by 4 publications

References 18 publications

Social stability and extended social balance—Quantifying the role of inactive links in social networks

Social stability and extended social balance—Quantifying the role of inactive links in social networks

Information processing by networks of quantum decision makers

Dyadic imbalance in networks

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