A Logical Analysis of the Interplay Between Social Influence and Friendship Selection

Smets, Sonja; Velázquez–Quesada, Fernando R.

doi:10.1007/978-3-030-38808-9_5

Cited by 3 publications

(12 citation statements)

References 21 publications

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“…Agents might start adopting new features if enough of their neighbors had already adopted them before the update. Note that, while [19,1] consider updates in which agents might start abandoning previously adopted features, here we restrict ourselves to the case in which agents are not allowed to start unadopting features, similarly as in [2]. The diffusion update does not affect the network structure.…”

Section: Logic Of Asynchronous and Synchronous Network Changesmentioning

confidence: 99%

“…In probabilistic models, it is usual to assume that agents who are more similar are more likely to connect than those who are less similar [23,4]. In deterministic models, this has been translated by a similarity threshold: two agents get connected as soon as they are similar enough [18,20,19,21].…”

Section: Introductionmentioning

confidence: 99%

“…Both types of changes have been addressed in logic. Indeed, a number of logical frameworks has flourished to reason about threshold-based social influence [2,6,5,16,14], and about threshold-based link formation [18,20,19,21]. Yet, to the exception of [11,15,1], either the two aspects have been treated separately [22,18,20,19,21,10], or the two types of changes have been taken to happen one after the other [19].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, a number of logical frameworks has flourished to reason about threshold-based social influence [2,6,5,16,14], and about threshold-based link formation [18,20,19,21]. Yet, to the exception of [11,15,1], either the two aspects have been treated separately [22,18,20,19,21,10], or the two types of changes have been taken to happen one after the other [19]. To our knowledge, only [1] provides a logic capturing specifically simultaneous changes of the network structure and the state of the agents.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we introduce a closely related framework that, similarly to [1] combines three dynamic operators: one corresponding to the change of the network structure only, one corresponding to the change of the agents feature (opinion/behavior/state) only, and one corresponding to both changes at the same time, but restricting ourselves to monotonic changes. We then tackle for this monotonic setting an open question by [19,1] in the literature: Can different sequences of dynamic operators be reduced to one another?…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparing Social Network Dynamic Operators

Baccini,

Christoff

2023

Electron. Proc. Theor. Comput. Sci.

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Numerous logics have been developed to reason either about threshold-induced opinion diffusion in a network, or about similarity-driven network structure evolution, or about both. In this paper, we first introduce a logic containing different dynamic operators to capture changes that are 'asynchronous' (opinion change only, network-link change only) and changes that are 'synchronous' (both at the same time). Second, we show that synchronous operators cannot, in general, be replaced by asynchronous operators and vice versa. Third, we characterise the class of models on which the synchronous operator can be reduced to sequences of asynchronous operators.

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Section: Logic Of Asynchronous and Synchronous Network Changesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Social Network Dynamic Operators

Baccini,

Christoff

2023

Electron. Proc. Theor. Comput. Sci.

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Dynamic Logics of Diffusion and Link Changes on Social Networks

Baccini,

Christoff,

Verbrugge

2024

Stud Logica

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This paper introduces a comprehensive logical framework to reason about threshold-driven diffusion and threshold-driven link change in social networks. It considers both monotonic dynamics, where agents can only adopt new features and create new connections, and non-monotonic dynamics, where agents may also abandon features or cut ties. Three types of operators are combined: one capturing diffusion only, one capturing link change only, and one capturing both at the same time. We first characterise the models on which diffusion of a unique feature and link change stabilise, whilst discussing salient properties of stable models with multiple spreading features. Second, we show that our operators (and any combination of them) are irreplaceable, in the sense that the sequences of model updates expressed by a combination of operators cannot always be expressed using any other operators. Finally, we analyse classes of models on which some operators can be replaced.

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Detecting bots with temporal logic

Pedersen,

Slavkovik,

Smets

2023

Synthese

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Social bots are computer programs that act like human users on social media platforms. Social bot detection is a rapidly growing field dominated by machine learning approaches. In this paper, we propose a complementary method to machine learning by exploring bot detection as a model checking problem. We introduce Temporal Network Logic (TNL) which we use to specify social networks where agents can post and follow each other. Using this logic, we formalize different types of social bot behavior with formulas that are satisfied in a model of a network with bots. We also consider an extension of the logic where we explore the expressive power of including elements from hybrid logic in our framework. We give model checking algorithms for TNL and its hybrid extension, and show that the complexity of the former is in p and the latter in pspace.

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A Logical Analysis of the Interplay Between Social Influence and Friendship Selection

Cited by 3 publications

References 21 publications

Comparing Social Network Dynamic Operators

Comparing Social Network Dynamic Operators

Dynamic Logics of Diffusion and Link Changes on Social Networks

Detecting bots with temporal logic

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