Detection of the elite structure in a virtual multiplex social system by means of a generalized KK-core

Elites are subgroups of individuals within a society that have the ability and means to influence, lead, govern, and shape societies. Members of elites are often well connected individuals, which enables them to impose their influence to many and to quickly gather, process, and spread information. Here we argue that elites are not only composed of highly connected individuals, but also of intermediaries connecting hubs to form a cohesive and structured elite-subgroup at the core of a social network. For this purpose we present a generalization of the $K$-core algorithm that allows to identify a social core that is composed of well-connected hubs together with their `connectors'. We show the validity of the idea in the framework of a virtual world defined by a massive multiplayer online game, on which we have complete information of various social networks. Exploiting this multiplex structure, we find that the hubs of the generalized $K$-core identify those individuals that are high social performers in terms of a series of indicators that are available in the game. In addition, using a combined strategy which involves the generalized $K$-core and the recently introduced $M$-core, the elites of the different 'nations' present in the game are perfectly identified as modules of the generalized $K$-core. Interesting sudden shifts in the composition of the elite cores are observed at deep levels. We show that elite detection with the traditional $K$-core is not possible in a reliable way. The proposed method might be useful in a series of more general applications, such as community detection.Comment: 13 figures, 3 tables, 19 pages. Accepted for publication in PLoS ON

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

The domination number of on-line social networks and random geometric graphs

We consider the domination number for on-line social networks, both in a stochastic network model, and for real-world, networked data. Asymptotic sublinear bounds are rigorously derived for the domination number of graphs generated by the memoryless geometric protean random graph model. We establish sublinear bounds for the domination number of graphs in the Facebook 100 data set, and these bounds are well-correlated with those predicted by the stochastic model. In addition, we derive the asymptotic value of the domination number in classical random geometric graphs

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201

Multiplex core-periphery organization of the human connectome

The behavior of many complex systems is determined by a core of densely interconnected units. While many methods are available to identify the core of a network when connections between nodes are all of the same type, a principled approach to define the core when multiple types of connectivity are allowed is still lacking. Here we introduce a general framework to define and extract the core-periphery structure of multi-layer networks by explicitly taking into account the connectivity of the nodes at each layer. We show how our method works on synthetic networks with different size, density, and overlap between the cores at the different layers. We then apply the method to multiplex brain networks whose layers encode information both on the anatomical and the functional connectivity among regions of the human cortex. Results confirm the presence of the main known hubs, but also suggest the existence of novel brain core regions that have been discarded by previous analysis which focused exclusively on the structural layer. Our work is a step forward in the identification of the core of the human connectome, and contributes to shed light to a fundamental question in modern neuroscience.Comment: Main text (12 pages, 5 figures) + Supplementary material (6 pages, 5 figures, 1 table

Measuring and modeling correlations in multiplex networks

The authors acknowledge the support of the EU Seventh Framework Programme through the Project LASAGNE (Grant No. FP7-ICT-318132). This research utilized Queen Mary’s MidPlus computational facilities, supported by QMUL Research-IT and funded by EPSRC Grant No. EP/K000128/1

Cosmopolitan culture and counterculture among Chinese youth: face-to-face communities in the smartphone era

Young Chinese people have come of age in a communicative environment that is radically new, involving near-pervasive mobile broadband internet access and unprecedented exposure to global media. I employ a mix of ethnographic and computational methods to compare two groups of cosmopolitan Chinese youth – elite university students and subcultural bohemians – to explore the political implications of their cosmopolitan communications. The cosmopolitanism of Chinese youth, understood as both communicative diversity and globalized cultural engagement, is shaped in divergent ways by the influence of China’s orthodox Confucian and heterodox cultural traditions, with marked implications for patterns of online and offline communication. Constraints imposed by the university environment and Confucian social norms embed elite students in homogeneous networks that their extensive online communications do little to diversify. Exposure to the competing perspectives of global and domestic news and academic content generates both a normative relativism and a sophisticated grasp of practical political possibilities and constraints. This supports a hierarchical and pragmatic politics in which both national interests and those of their own social echelon, including progressive identity claims, are seen as being furthered by meritocratic authoritarianism. By contrast, bohemian proclivities for free-wheeling face-to-face interaction embed them in heterogeneous, cross-cutting networks, within which they synthesize discontents from diverse areas of Chinese society; combined with the influence of the heterodox tradition and the oppositional symbolic repertoires of global subcultures, this results in an egalitarian and reductively idealistic politics that supports opposition to the Party-State and its authoritarian system. The dominance of elite students’ orthodox cosmopolitanism suggests that the internet-mediated, globalized communications of Chinese youth constitute little immediate threat to the authoritarian system. However, the increasing scale and influence of bohemian heterodox cosmopolitanism and its idealistic politics, driven by factors beyond the control of Party-State, may ultimately undermine the manageability of Chinese youth