384 research outputs found
Temperature in complex networks
Various statistical-mechanics approaches to complex networks have been proposed to describe expected topological properties in terms of ensemble averages. Here we extend this formalism by introducing the fundamental concept of graph temperature, controlling the degree of topological optimization of a network. We recover the temperature-dependent version of various important models as particular cases of our approach, and show examples where, remarkably, the onset of a percolation transition, a scale-free degree distribution, correlations and clustering can be understood as natural properties of an optimized (low-temperature) topology. We then apply our formalism to real weighted networks and we compute their temperature, finding that various techniques used to extract information from complex networks are again particular cases of our approach
Optimal scales in weighted networks
The analysis of networks characterized by links with heterogeneous intensity
or weight suffers from two long-standing problems of arbitrariness. On one
hand, the definitions of topological properties introduced for binary graphs
can be generalized in non-unique ways to weighted networks. On the other hand,
even when a definition is given, there is no natural choice of the (optimal)
scale of link intensities (e.g. the money unit in economic networks). Here we
show that these two seemingly independent problems can be regarded as
intimately related, and propose a common solution to both. Using a formalism
that we recently proposed in order to map a weighted network to an ensemble of
binary graphs, we introduce an information-theoretic approach leading to the
least biased generalization of binary properties to weighted networks, and at
the same time fixing the optimal scale of link intensities. We illustrate our
method on various social and economic networks.Comment: Accepted for presentation at SocInfo 2013, Kyoto, 25-27 November 2013
(http://www.socinfo2013.org
Low-temperature behaviour of social and economic networks
Real-world social and economic networks typically display a number of
particular topological properties, such as a giant connected component, a broad
degree distribution, the small-world property and the presence of communities
of densely interconnected nodes. Several models, including ensembles of
networks also known in social science as Exponential Random Graphs, have been
proposed with the aim of reproducing each of these properties in isolation.
Here we define a generalized ensemble of graphs by introducing the concept of
graph temperature, controlling the degree of topological optimization of a
network. We consider the temperature-dependent version of both existing and
novel models and show that all the aforementioned topological properties can be
simultaneously understood as the natural outcomes of an optimized,
low-temperature topology. We also show that seemingly different graph models,
as well as techniques used to extract information from real networks, are all
found to be particular low-temperature cases of the same generalized formalism.
One such technique allows us to extend our approach to real weighted networks.
Our results suggest that a low graph temperature might be an ubiquitous
property of real socio-economic networks, placing conditions on the diffusion
of information across these systems
Automated Negotiations Under Uncertain Preferences
Automated Negotiation is an emerging field of electronic markets and multi-agent system research. Market engineers are faced in this connection with computational as well as economic issues, such as individual rationality and incentive compatibility. Most literature is focused on autonomous agents and negotiation protocols regarding these issues. However, common protocols show two deficiencies: (1) neglected consideration of agents’ incentives to strive for social welfare, (2) underemphasised acknowledgement that agents build their decision upon preference information delivered by human principals. Since human beings make use of heuristics for preference elicitation, their preferences are subject to informational uncertainty. The contribution of this paper is the proposition of a research agenda that aims at overcoming these research deficiencies. Our research agenda draws theoretically and methodologically on auctions, iterative bargaining, and fuzzy set theory. We complement our agenda with simulation-based preliminary results regarding differences in the application of auctions and iterative bargaining
Role of Natural Killer and Dendritic Cell Crosstalk in Immunomodulation by Commensal Bacteria Probiotics
A cooperative dialogue between natural killer (NK) cells and dendritic cells (DCs) has been elucidated in the last years. They help each other to acquire their complete functions, both in the periphery and in the secondary lymphoid organs. Thus, NK cells' activation by dendritic cells allows the killing of transformed or infected cells in the periphery but may also be important for the generation of adaptive immunity. Indeed, it has been shown that NK cells may play a key role in polarizing a Th1 response upon interaction with DCs exposed to microbial products. This regulatory role of DC/NK cross-talk is of particular importance at mucosal surfaces such as the intestine, where the immune system exists in intimate association with commensal bacteria such as lactic acid bacteria (LAB). We here review NK/DC interactions in the presence of gut-derived commensal bacteria and their role in bacterial strain-dependent immunomodulatory effects. We particularly aim to highlight the ability of distinct species of commensal bacterial probiotics to differently affect the outcome of DC/NK cross-talk and consequently to differently influence the polarization of the adaptive immune response
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