ترغب بنشر مسار تعليمي؟ اضغط هنا

The Temporal Rich Club Phenomenon

90   0   0.0 ( 0 )
 نشر من قبل Alain Barrat
 تاريخ النشر 2021
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

Identifying the hidden organizational principles and relevant structures of networks representing complex physical systems is fundamental to understand their properties. To this aim, uncovering the structures involving a networks prominent nodes in a network is an effective approach. In temporal networks, the simultaneity of connections is crucial for temporally stable structures to arise. We thus propose here a novel measure to quantitatively investigate the tendency of well connected nodes to form simultaneous and stable structures in a temporal network. We refer to this tendency, when observed, as the temporal rich club phenomenon. We illustrate the interest of this concept by analyzing diverse data sets under this lens, and showing how it enables a new perspective on their temporal patterns, from the role of cohesive structures in relation to processes unfolding on top of the network to the study of specific moments of interest in the evolution of the network.


قيم البحث

اقرأ أيضاً

For many complex networks present in nature only a single instance, usually of large size, is available. Any measurement made on this single instance cannot be repeated on different realizations. In order to detect significant patterns in a real--wor ld network it is therefore crucial to compare the measured results with a null model counterpart. Here we focus on dense and weighted networks, proposing a suitable null model and studying the behaviour of the degree correlations as measured by the rich-club coefficient. Our method solves an existing problem with the randomization of dense unweighted graphs, and at the same time represents a generalization of the rich--club coefficient to weighted networks which is complementary to other recently proposed ones.
Rich-club ordering and the dyadic effect are two phenomena observed in complex networks that are based on the presence of certain substructures composed of specific nodes. Rich-club ordering represents the tendency of highly connected and important e lements to form tight communities with other central elements. The dyadic effect denotes the tendency of nodes that share a common property to be much more interconnected than expected. In this study, we consider the interrelation between these two phenomena, which until now have always been studied separately. We contribute with a new formulation of the rich-club measures in terms of the dyadic effect. Moreover, we introduce certain measures related to the analysis of the dyadic effect, which are useful in confirming the presence and relevance of rich-clubs in complex networks. In addition, certain computational experiences show the usefulness of the introduced quantities with regard to different classes of real networks.
Core-periphery networks are structures that present a set of central and densely connected nodes, namely the core, and a set of non-central and sparsely connected nodes, namely the periphery. The rich-club refers to a set in which the highest degree nodes show a high density of connections. Thus, a network that displays a rich-club can be interpreted as a core-periphery network in which the core is made up by a number of hubs. In this paper, we test the resilience of networks showing a progressively denser rich-club and we observe how this structure is able to affect the network measures in terms of both cohesion and efficiency in information flow. Additionally, we consider the case in which, instead of making the core denser, we add links to the periphery. These two procedures of core and periphery thickening delineate a decision process in the placement of new links and allow us to conduct a scenario analysis that can be helpful in the comprehension and supervision of complex networks under the resilience perspective. The advantages of the two procedures, as well as their implications, are discussed in relation to both network effciency and node heterogeneity.
The study of the weak-ties phenomenon has a long and well documented history, research into the application of this social phenomenon has recently attracted increasing attention. However, further exploration of the reasons behind the weak-ties phenom enon is still challenging. Fortunately, data-driven network science provides a novel way with substantial explanatory power to analyze the causal mechanism behind social phenomenon. Inspired by this perspective, we propose an approach to further explore the driving factors behind the temporal weak-ties phenomenon. We find that the obvious intuition underlying the weak-ties phenomenon is incorrect, and often large numbers of unknown mutual friends associated with these weak ties is one of the key reason for the emergence of the weak-ties phenomenon. In particular, for example scientific collaborators with weak ties prefer to be involved in direct collaboration rather than share ideas with mutual colleagues -- there is a natural tendency to collapse short strong chains of connection.
In our model, $n$ traders interact with each other and with a central bank; they are taxed on the money they make, some of which is dissipated away by corruption. A generic feature of our model is that the richest trader always wins by consuming all the others: another is the existence of a threshold wealth, below which all traders go bankrupt. The two-trader case is examined in detail,in the socialist and capitalist limits, which generalise easily to $n>2$. In its mean-field incarnation, our model exhibits a two-time-scale glassy dynamics, as well as an astonishing universality.When preference is given to local interactions in finite neighbourhoods,a novel feature emerges: instead of at most one overall winner in the system,finite numbers of winners emerge, each one the overlord of a particular region.The patterns formed by such winners (metastable states) are very much a consequence of initial conditions, so that the fate of the marketplace is ruled by its past history; hysteresis is thus also manifested.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا