arXiv subjects

Federico Battiston

Publications and source records attributed to Federico Battiston.

81 records · Page 5Linked to original sources

arXiv2017

Layered social influence promotes multiculturality in the Axelrod model

Why is our society multicultural? Based on the two mechanisms of homophily and social influence, the classical model for the dissemination of cultures proposed by Axelrod predicts the existence of a fragmented regime where different cultures can coexist in a social network. However, in such model the multicultural regime is achievable only when a high number of cultural traits is present, and is not robust against cultural drift, i.e. the spontaneous mutations of agents' traits. In real systems, social influence is inherently organised in layers, meaning that individuals tend to diversify their connections according to the topic on which they interact. In this work we show that the observed persistence of multiculturality in real-world social systems is a natural consequence of the layered organisation of social influence. We find that the critical number of cultural traits that separates the monocultural and the multicultural regimes depends on the redundancy of pairwise connections across layers. Surprisingly, for low values of structural redundancy the system is always in a multicultural state, independently on the number of traits, and is robust to the presence of cultural drift. Moreover, we show that layered social influence allows the coexistence of different levels of consensus on different topics. The insight obtained from simulations on synthetic graphs are confirmed by the analysis of two real-world social networks, where the multicultural regime persists even for a very small number of cultural traits, suggesting that the layered organisation of social interactions might indeed be at the heart of multicultural societies.

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arXiv2016

Multilayer motif analysis of brain networks

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system, and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows to perform an analysis of the human brain where the structural and functional layers are considered together. In this work we describe how to classify the subgraphs of a multiplex network, and we extend motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between structure and function in the human brain.

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arXiv2016

The new challenges of multiplex networks: measures and models

What do societies, the Internet, and the human brain have in common? They are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons, rather than the properties of the units themselves. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.

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arXiv2016

The Role of Noise in the Spatial Public Goods Game

In this work we aim to analyze the role of noise in the spatial Public Goods Game, one of the most famous games in Evolutionary Game Theory. The dynamics of this game is affected by a number of parameters and processes, namely the topology of interactions among the agents, the synergy factor, and the strategy revision phase. The latter is a process that allows agents to change their strategy. Notably, rational agents tend to imitate richer neighbors, in order to increase the probability to maximize their payoff. By implementing a stochastic revision process, it is possible to control the level of noise in the system, so that even irrational updates may occur. In particular, in this work we study the effect of noise on the macroscopic behavior of a finite structured population playing the Public Goods Game. We consider both the case of a homogeneous population, where the noise in the system is controlled by tuning a parameter representing the level of stochasticity in the strategy revision phase, and a heterogeneous population composed of a variable proportion of rational and irrational agents. In both cases numerical investigations show that the Public Goods Game has a very rich behavior which strongly depends on the amount of noise in the system and on the value of the synergy factor. To conclude, our study sheds a new light on the relations between the microscopic dynamics of the Public Goods Game and its macroscopic behavior, strengthening the link between the field of Evolutionary Game Theory and statistical physics.

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arXiv2016

Efficient exploration of multiplex networks

Efficient techniques to navigate networks with local information are fundamental to sample large-scale online social systems and to retrieve resources in peer-to-peer systems. Biased random walks, i.e. walks whose motion is biased on properties of neighbouring nodes, have been largely exploited to design smart local strategies to explore a network, for instance by constructing maximally mixing trajectories or by allowing an almost uniform sampling of the nodes. Here we introduce and study biased random walks on multiplex networks, graphs where the nodes are related through different types of links organised in distinct and interacting layers, and we provide analytical solutions for their long-time properties, including the stationary occupation probability distribution and the entropy rate. We focus on degree-biased random walks and distinguish between two classes of walks, namely those whose transition probability depends on a number of parameters which is extensive in the number of layers, and those whose motion depends on intrinsically multiplex properties of the neighbouring nodes. We analyse the effect of the structure of the multiplex network on the steady-state behaviour of the walkers, and we find that heterogeneous degree distributions as well as the presence of inter-layer degree correlations and edge overlap determine the extent to which a multiplex can be efficiently explored by a biased walk. Finally we show that, in real-world multiplex transportation networks, the trade-off between efficient navigation and resilience to link failure has resulted into systems whose diffusion properties are qualitatively different from those of appropriately randomised multiplex graphs. This fact suggests that multiplexity is an important ingredient to include in the modelling of real-world systems.

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arXiv2016

Emergence of multiplex communities in collaboration networks

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

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arXiv2015

Interplay between consensus and coherence in a model of interacting opinions

The formation of agents' opinions in a social system is the result of an intricate equilibrium among several driving forces. On the one hand, the social pressure exerted by peers favours the emergence of local consensus. On the other hand, the concurrent participation of agents to discussions on different topics induces each agent to develop a coherent set of opinions across all the topics in which he is active. Moreover, the pervasive action of external stimuli, such as mass media, pulls the entire population towards a specific configuration of opinions on different topics. Here we propose a model in which agents with interrelated opinions, interacting on several layers representing different topics, tend to spread their own ideas to their neighbourhood, strive to maintain internal coherence, due to the fact that each agent identifies meaningful relationships among its opinions on the different topics, and are at the same time subject to external fields, resembling the pressure of mass media. We show that the presence of heterogeneity in the internal coupling assigned by agents to their different opinions allows to obtain states with mixed levels of consensus, still ensuring that all the agents attain a coherent set of opinions. Furthermore, we show that all the observed features of the model are preserved in the presence of thermal noise up to a critical temperature, after which global consensus is no longer attainable. This suggests the relevance of our results for real social systems, where noise is inevitably present in the form of information uncertainty and misunderstandings. The model also demonstrates how mass media can be effectively used to favour the propagation of a chosen set of opinions, thus polarising the consensus of an entire population.

physics.soc-ph↗

arXiv2015

Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications.

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arXiv2014

Structural measures for multiplex networks

Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes, rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular we propose a series of measures to characterize the multiplexicity of the systems in terms of: i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, ii) local properties such as the clustering coefficient and the transitivity, iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuine multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications and business relationships.

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