Category: Papers

Ranking online social users by their Influence

Anastasios Giovanidis, Bruno Baynat, Clémence Magnien & Antoine Vendeville
IEEE/ACM Transactions on Networking ( Early Access ) (2021)

Date of Publication: 08 June 2021

This work introduces an original mathematical model to analyze the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with his/her own Wall and Newsfeed, and has his/her own individual self-posting and re-posting activity. As a main result using the developed model, the probabilities that posts originating from a given user are found on the Wall and Newsfeed of any other can be derived in closed form. These are the solution of a linear system of equations, which can be resolved iteratively. In fact, the new model is very flexible with respect to the modeling assumptions. Using the probabilities derived from the solution, a new measure of per-user influence over the entire network is defined, named the Ψ-score, which combines the user position on the graph with user (re-)posting activity. In the homogeneous case where all users have the same activity rates, it is shown that a variant of the Ψ-score is equal to PageRank. Furthermore, the new model and its Ψ-score are compared against the empirical influence measured from very large data traces (Twitter, Weibo). The results illustrate that these new tools can accurately rank influencers with asymmetric (re-)posting activity for such real world applications.

Read the full article at:  ieeexplore.ieee.org

Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity

Georgina Montserrat Reséndiz-Benhumea, Ekaterina Sangati, Federico Sangati, Soheil Keshmiri and Tom Froese

The social brain hypothesis proposes that enlarged brains have evolved in response to the increasing cognitive demands that complex social life in larger groups places on primates and other mammals. However, this reasoning can be challenged by evidence that brain size has decreased in the evolutionary transitions from solitary to social larger groups in the case of Neolithic humans and some eusocial insects. Different hypotheses can be identified in the literature to explain this reduction in brain size. We evaluate some of them from the perspective of recent approaches to cognitive science, which support the idea that the basis of cognition can span over brain, body, and environment. Here we show through a minimal cognitive model using an evolutionary robotics methodology that the neural complexity, in terms of neural entropy and degrees of freedom of neural activity, of smaller-brained agents evolved in social interaction is comparable to the neural complexity of larger-brained agents evolved in solitary conditions. The nonlinear time series analysis of agents’ neural activity reveals that the decoupled smaller neural network is intrinsically lower dimensional than the decoupled larger neural network. However, when smaller-brained agents are interacting, their actual neural complexity goes beyond its intrinsic limits achieving results comparable to those obtained by larger-brained solitary agents. This suggests that the smaller-brained agents are able to enhance their neural complexity through social interaction, thereby offsetting the reduced brain size.

Read the full article at: www.frontiersin.org

Designing temporal networks that synchronize under resource constraints

Yuanzhao Zhang & Steven H. Strogatz 
Nature Communications volume 12, Article number: 3273 (2021)

Being fundamentally a non-equilibrium process, synchronization comes with unavoidable energy costs and has to be maintained under the constraint of limited resources. Such resource constraints are often reflected as a finite coupling budget available in a network to facilitate interaction and communication. Here, we show that introducing temporal variation in the network structure can lead to efficient synchronization even when stable synchrony is impossible in any static network under the given budget, thereby demonstrating a fundamental advantage of temporal networks. The temporal networks generated by our open-loop design are versatile in the sense of promoting synchronization for systems with vastly different dynamics, including periodic and chaotic dynamics in both discrete-time and continuous-time models. Furthermore, we link the dynamic stabilization effect of the changing topology to the curvature of the master stability function, which provides analytical insights into synchronization on temporal networks in general. In particular, our results shed light on the effect of network switching rate and explain why certain temporal networks synchronize only for intermediate switching rate.

Read the full article at: www.nature.com

Conspiracy of Corporate Networks in Corruption Scandals

J. R. Nicolás-Carlock & I. Luna-Pla

Front. Phys

Corruption in public procurement transforms state institutions into private entities where public resources get diverted for the benefit of a few. On this matter, much of the discussion centers on the legal fulfillment of the procurement process, while there are fewer formal analyses related to the corporate features which are most likely to signal organized crime and corruption. The lack of systematic evidence on this subject has the potential to bias our understanding of corruption, making it overly focused on the public sector. Nevertheless, corruption scandals worldwide tell of the importance of taking a better look at the misuse and abuse of corporations for corrupt purposes. In this context, the research presented here seeks to contribute to the understanding of the criminal conspiracy of companies involved in public procurement corruption scandals under a network and complexity science perspective. To that end, we make use of a unique dataset of the corporate ownership and management information of four important and recently documented cases of corruption in Mexico, where hundreds of companies were used to embezzle billions of dollars. Under a bipartite network approach, we explore the relations between companies and their personnel (shareholders, legal representatives, administrators, and commissioners) in order to characterize their static and dynamic networked structure. In terms of organized crime and using different network properties, we describe how these companies connect with each other due to the existence of shared personnel with role multiplicity, leading to very different conspiracy networks. To best quantify this behavior, we introduce a heuristic network-based conspiracy indicator that together with other network metrics describes the differences and similarities among the networks associated with each corruption case. Finally, we discuss some public policy elements that might be needed to be considered in anti-corruption efforts related to corporate organized crime.

Read the full article at: www.frontiersin.org

Is Green Development an Oxymoron?

Ricardo Hausmann

Decarbonization will transform global production and trade patterns so radically that new growth opportunities are bound to arise for the Global South. The goal for them should not be to stop global warming by restricting domestic emissions, but rather to carve out a role for themselves in a rapidly greening world economy.

Read the full article at: www.project-syndicate.org