PopRank: Ranking pages’ impact and users’ engagement on Facebook

Zaccaria, Andrea; Vicario, Michela Del; Scala, Antonio; Pietronero, L.

doi:10.1371/journal.pone.0211038

Cited by 9 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Country competitiveness Field complexity [196] Social User-page engagement in Facebook User engagement Page impact [197] Ecological Plant-pollinator Pollinator importance Plant vulnerability [71] Table 5: Applications of the fitness-complexity algorithm and its variants to diverse systems. For each system, we provide a brief interpretation of both Fitness and Complexity score, and we refer to the mentioned references for all the details.…”

Section: Countryresearch Fieldmentioning

confidence: 99%

“…At the same time, the rankings obtained with γ > 1 tend to be more sensitive to structural perturbations of the network's structure [72], which is a drawback especially for systems where a non-negligible fraction of the links might be unreliable, such as the World Trade [45,198]. A different generalization of the fitness-algorithm was introduced by Zaccaria et al [197] to rank users' engagement and pages' impact in Facebook; they found that the resulting algorithm (which they called PopRank ) can reliably predict the future activity of a Facebook page.…”

Section: Countryresearch Fieldmentioning

confidence: 99%

See 1 more Smart Citation

Nestedness in complex networks: Observation, emergence, and implications

Mariani,

Ren,

Bascompte

et al. 2019

Preprint

View full text Add to dashboard Cite

The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks call for an explanation of their emergence and an understanding of their potential systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness can be described as the tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Known since more than 80 years in biogeography, nestedness has been found in systems as diverse as ecological mutualistic organizations, world trade, inter-organizational relations, among many others. This review article focuses on three main pillars: the existing methodologies to observe nestedness in networks; the main theoretical mechanisms conceived to explain the emergence of nestedness in ecological and socio-economic networks; the implications of a nested topology of interactions for the stability and feasibility of a given interacting system. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. Nestedness was found to emerge both in bipartite networks and, more recently, in unipartite ones; this review is the first comprehensive attempt to unify both streams of studies, usually disconnected from each other. We believe that the truly interdisciplinary endeavor -while rooted in a complex systems perspective -may inspire new models and algorithms whose realm of application will undoubtedly transcend disciplinary boundaries.

show abstract

Section: Countryresearch Fieldmentioning

confidence: 99%

Section: Countryresearch Fieldmentioning

confidence: 99%

Nestedness in complex networks: Observation, emergence, and implications

Mariani,

Ren,

Bascompte

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Compared to the Methods of Reflections [6] and the equivalent Economic Complexity Index 5 , the fitness-complexity better quantifies countries' and products' structural importance in the country-product bipartite network [7]; it substantially improves the GDP growth predictability [8]; it identifies correctly high-growth countries such as China and India, which are far from the top-ranked countries by ECI 6 . Because of its effectiveness, the FC algorithm itself has seen modifications [7,9,10] and applications in other domains [11][12][13][14]. Compared to its variants such as the minimal extremal metric [9] and the generalized fitness-complexity algorithm [7], the original fitness-complexity algorithm is substantially more robust with respect to noisy input data, which makes it better suited for the analysis of world trade data [7,9].…”

Section: Introductionmentioning

confidence: 99%

Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms

Mazzilli,

Mariani,

Morone

et al. 2024

J. Phys. Complex.

View full text Add to dashboard Cite

We uncover the connection between the Fitness-Complexity algorithm, developed in the economic complexity field, and the Sinkhorn-Knopp algorithm, widely used in diverse domains ranging from computer science and mathematics to economics. Despite minor formal differences between the two methods, both converge to the same fixed-point solution up to normalization.The discovered connection allows us to derive a rigorous interpretation of the Fitness and the Complexity metrics as the potentials of a suitable energy function.Under this interpretation, high-energy products are unfeasible for low-fitness countries, which explains why the algorithm is effective at displaying nested patterns in bipartite networks.We also show that the proposed interpretation reveals the scale invariance of the Fitness-Complexity algorithm, which has practical implications for the algorithm's implementation in different datasets.Further, analysis of empirical trade data under the new perspective reveals three categories of countries that might benefit from different development strategies.

show abstract

“…When the contents consumed by users pertain to a certain narrative, such as in the case of political news, it has been pointed out selective exposure dominates users' attention patterns [1,2]. We tend to select information adhering to our system of beliefs and to ignore dissenting information [3,4,5,6]. However, other factors may influence content selection, especially if we consider the huge and heterogeneous volume of recreational material present online.…”

Section: Introductionmentioning

confidence: 99%

Drifts and Shifts: Characterizing the Evolution of Users Interests on Reddit

Valensise,

Cinelli,

Galeazzi

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Selective exposure is the main driver for the economy of attention when consuming online contents. We select information adhering to our system of beliefs and ignore dissenting information. However, even personal interest is likely to play a role in determining our attention patterns. To understand in more detail the dynamics of interest-driven choices, we address the evolution of users' interest on Reddit by means of an analysis on more than 7 million of users. We observe that the lifetime distribution of users on subreddits (online thematic communities) is 'short' with respect to the observation period. Furthermore, users tend to be active on a very limited number of subreddits with respect to the wide offer of the platform. These aspects indicate the presence of a migrating behavior of users among subreddits. To characterize this phenomenon we propose a metric based on a geometrical encoding of the 'interest space' of the user. The movement of users across subreddits is characterized by a bursty trend, made of sudden variations. The most frequent of them take place between recreational subreddits and those more related to news and politics. We describe this kind of activity with two behaviors called interest drift and interest shift. Our results suggest that selective exposure and personal interest coexist in driving content consumption.

show abstract

PopRank: Ranking pages’ impact and users’ engagement on Facebook

Cited by 9 publications

References 25 publications

Nestedness in complex networks: Observation, emergence, and implications

Nestedness in complex networks: Observation, emergence, and implications

Equivalence between the Fitness-Complexity and the Sinkhorn-Knopp algorithms

Drifts and Shifts: Characterizing the Evolution of Users Interests on Reddit

Contact Info

Product

Resources

About