24 research outputs found
Emergence of soft communities from geometric preferential attachment
All real networks are different, but many have some structural properties in common. There seems to be no consensus on what the most common properties are, but scale-free degree distributions, strong clustering, and community structure are frequently mentioned without question. Surprisingly, there exists no simple generative mechanism explaining all the three properties at once in growing networks. Here we show how latent network geometry coupled with preferential attachment of nodes to this geometry fills this gap. We call this mechanism geometric preferential attachment (GPA), and validate it against the Internet. GPA gives rise to soft communities that provide a different perspective on the community structure in networks. The connections between GPA and cosmological models, including inflation, are also discussed
Collective navigation of complex networks: Participatory greedy routing
Many networks are used to transfer information or goods, in other words, they
are navigated. The larger the network, the more difficult it is to navigate
efficiently. Indeed, information routing in the Internet faces serious
scalability problems due to its rapid growth, recently accelerated by the rise
of the Internet of Things. Large networks like the Internet can be navigated
efficiently if nodes, or agents, actively forward information based on hidden
maps underlying these systems. However, in reality most agents will deny to
forward messages, which has a cost, and navigation is impossible. Can we design
appropriate incentives that lead to participation and global navigability?
Here, we present an evolutionary game where agents share the value generated by
successful delivery of information or goods. We show that global navigability
can emerge, but its complete breakdown is possible as well. Furthermore, we
show that the system tends to self-organize into local clusters of agents who
participate in the navigation. This organizational principle can be exploited
to favor the emergence of global navigability in the system.Comment: Supplementary Information and Videos:
https://koljakleineberg.wordpress.com/2016/11/14/collective-navigation-of-complex-networks-participatory-greedy-routing
Hidden geometric correlations in real multiplex networks
Real networks often form interacting parts of larger and more complex
systems. Examples can be found in different domains, ranging from the Internet
to structural and functional brain networks. Here, we show that these multiplex
systems are not random combinations of single network layers. Instead, they are
organized in specific ways dictated by hidden geometric correlations between
the individual layers. We find that these correlations are strong in different
real multiplexes, and form a key framework for answering many important
questions. Specifically, we show that these geometric correlations facilitate:
(i) the definition and detection of multidimensional communities, which are
sets of nodes that are simultaneously similar in multiple layers; (ii) accurate
trans-layer link prediction, where connections in one layer can be predicted by
observing the hidden geometric space of another layer; and (iii) efficient
targeted navigation in the multilayer system using only local knowledge, which
outperforms navigation in the single layers only if the geometric correlations
are sufficiently strong. Our findings uncover fundamental organizing principles
behind real multiplexes and can have important applications in diverse domains.Comment: Supplementary Materials available at
http://www.nature.com/nphys/journal/v12/n11/extref/nphys3812-s1.pd
Growing graphs with addition of communities
Paper proposes a model of large networks based on a random preferential
attachment graph with addition of complete subgraphs (cliques). The proposed
model refers to models of random graphs following the nonlinear preferential
attachment rule and takes into account the possibility of
{\guillemotleft}adding{\guillemotright} entire communities of nodes to the
network. In the derivation of the relations that determine the vertex degree
distribution, the technique of finite-difference equations describing
stationary states of a graph is used. The obtained results are tested
empirically (by generating large graphs), special cases correspond to known
mathematical relations
Metric clusters in evolutionary games on scale-free networks
The evolution of cooperation in social dilemmas in structured populations has
been studied extensively in recent years. Whereas many theoretical studies have
found that a heterogeneous network of contacts favors cooperation, the impact
of spatial effects in scale-free networks is still not well understood. In
addition to being heterogeneous, real contact networks exhibit a high mean
local clustering coefficient, which implies the existence of an underlying
metric space. Here, we show that evolutionary dynamics in scale-free networks
self-organize into spatial patterns in the underlying metric space. The
resulting metric clusters of cooperators are able to survive in social dilemmas
as their spatial organization shields them from surrounding defectors, similar
to spatial selection in Euclidean space. We show that under certain conditions
these metric clusters are more efficient than the most connected nodes at
sustaining cooperation and that heterogeneity does not always favor--but can
even hinder--cooperation in social dilemmas. Our findings provide a new
perspective to understand the emergence of cooperation in evolutionary games in
realistic structured populations
Endogenous social distancing and its underappreciated impact on the epidemic curve
Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading