A Model for Scale-Free Networks: Application to Twitter

Aparicio, S.; Villazón-Terrazas, Javier; Alvarez, Gonzalo Rubén

doi:10.3390/e17085848

Cited by 64 publications

(40 citation statements)

References 26 publications

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“…Interface 14: 20170662 nodes were representing Facebook groups that are linked if the groups have common users. This is also the case for Twitter [100] and in author collaboration networks [86], where nodes represent authors that are linked if they have co-authored a paper.…”

Section: Network With Scale-invariant Node Distancementioning

confidence: 99%

Scale invariance in natural and artificial collective systems: a review

Khaluf

Ferrante

Simoens

et al. 2017

J. R. Soc. Interface.

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Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties.

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Section: Network With Scale-invariant Node Distancementioning

confidence: 99%

Scale invariance in natural and artificial collective systems: a review

Khaluf

Ferrante

Simoens

et al. 2017

J. R. Soc. Interface.

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“…In order to simulate Twitter-like networks, we used the model suggested in [4]. In that paper the authors show that their model is more suited to simulate the dynamic nature of social networks than the Barabási-Albert model.…”

Section: Test Results Of M G 2p On Scale-free Networkmentioning

confidence: 99%

Incentive-Compatible Diffusion

Babichenko

Dean

Tennenholtz

2018

Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18

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Our work bridges the literature on incentive-compatible mechanism design and the literature on diffusion algorithms. We introduce the study of finding an incentive-compatible (strategy-proof) mechanism for selecting an influential vertex in a directed graph (e.g. Twitter's network). The goal is to devise a mechanism with a bounded ratio between the maximal influence and the influence of the selected user, and in which no user can improve its probability of being selected by following or unfollowing other users. We introduce the Two Path mechanism which is based on the idea of selecting the vertex that is the first intersection of two independent random walks in the network. The Two Path mechanism is incentive compatible on directed acyclic graphs (DAGs), and has a finite approximation ratio on natural subfamilies of DAGs. Simulations indicate that this mechanism is suitable for practical uses.

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“…Note also that, formally, the MD for q candidates and N e f f messages corresponds to a q-state Potts model with zero coupling and q suitable external fields, while the MFP is characterized by a coupling that scales as 1/N e f f and no external field. Clearly, one could look for other mean-field Potts models with more general features able to interpolate between the MD and the MFP, as well as to take into account the scale-free character of the underlying Twitter network [20,21]. However, the MD and the MFP, which constitute the two simplest and somehow opposite mean-field models, turn out to be able to reproduce well the q(q + 1)/2 measured macroscopic correlations.…”

Section: Introductionmentioning

confidence: 99%

Critical states in Political Trends. How much reliable is a poll on Twitter?

Nicolao

Ostilli

2019

Physica A: Statistical Mechanics and its Applications

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In recent years, Twitter data related to political trends have tentatively been used to make predictions (poll) about several electoral events. Given q candidates for an election and a time-series of Twitts (short messages), one can extract the q mean trends and the q(q + 1)/2 Twitt-to-Twitt correlations, and look for the statistical models that reproduce these data. On the base of several electoral events and assuming a stationary regime, we find out the following: i) the maximization of the entropy singles out a microscopic model (single-Twitt-level) that coincides with a q-state Potts model having suitable couplings and external fields to be determined via an inverse problem from the two sets of data; ii) correlations decay as 1/N e f f , where N e f f is a small fraction of the mean number of Twitts; iii) the simplest statistical models that reproduce these correlations are the multinomial distribution (MD), characterized by q external fields, and the mean-field Potts model (MFP), characterized by one coupling; iv) remarkably, this coupling turns out to be always close to its critical value. This results in a MD or MFP model scenario that discriminates between cases in which polls are reliable and not reliable, respectively. More precisely, predictions based on polls should be avoided whenever the data maps to a MFP because anomalous large fluctuations (if q = 2) or sudden jumps (if q ≥ 3) in the trends might take place as a result of a second-order or a first-order phase transition of the MFP, respectively.

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A Model for Scale-Free Networks: Application to Twitter

Cited by 64 publications

References 26 publications

Scale invariance in natural and artificial collective systems: a review

Scale invariance in natural and artificial collective systems: a review

Incentive-Compatible Diffusion

Critical states in Political Trends. How much reliable is a poll on Twitter?

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