7,324 research outputs found
An introduction to Graph Data Management
A graph database is a database where the data structures for the schema
and/or instances are modeled as a (labeled)(directed) graph or generalizations
of it, and where querying is expressed by graph-oriented operations and type
constructors. In this article we present the basic notions of graph databases,
give an historical overview of its main development, and study the main current
systems that implement them
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
Adaptive Partitioning for Large-Scale Dynamic Graphs
Abstract—In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce network communication. The most notable way to achieve this goal is to partition the graph by minimizing the num-ber of edges that connect vertices assigned to different machines, while keeping the load balanced. However, real-world graphs are highly dynamic, with vertices and edges being constantly added and removed. Carefully updating the partitioning of the graph to reflect these changes is necessary to avoid the introduction of an extensive number of cut edges, which would gradually worsen computation performance. In this paper we show that performance degradation in dynamic graph processing systems can be avoided by adapting continuously the graph partitions as the graph changes. We present a novel highly scalable adaptive partitioning strategy, and show a number of refinements that make it work under the constraints of a large-scale distributed system. The partitioning strategy is based on iterative vertex migrations, relying only on local information. We have implemented the technique in a graph processing system, and we show through three real-world scenarios how adapting graph partitioning reduces execution time by over 50 % when compared to commonly used hash-partitioning. I
"Rotterdam econometrics": publications of the econometric institute 1956-2005
This paper contains a list of all publications over the period 1956-2005, as reported in the Rotterdam Econometric Institute Reprint series during 1957-2005.
Stabilizing Consensus with Many Opinions
We consider the following distributed consensus problem: Each node in a
complete communication network of size initially holds an \emph{opinion},
which is chosen arbitrarily from a finite set . The system must
converge toward a consensus state in which all, or almost all nodes, hold the
same opinion. Moreover, this opinion should be \emph{valid}, i.e., it should be
one among those initially present in the system. This condition should be met
even in the presence of an adaptive, malicious adversary who can modify the
opinions of a bounded number of nodes in every round.
We consider the \emph{3-majority dynamics}: At every round, every node pulls
the opinion from three random neighbors and sets his new opinion to the
majority one (ties are broken arbitrarily). Let be the number of valid
opinions. We show that, if , where is a
suitable positive constant, the 3-majority dynamics converges in time
polynomial in and with high probability even in the presence of an
adversary who can affect up to nodes at each round.
Previously, the convergence of the 3-majority protocol was known for
only, with an argument that is robust to adversarial errors. On
the other hand, no anonymous, uniform-gossip protocol that is robust to
adversarial errors was known for
Infrastructure for Engineered Emergence on Sensor/Actuator Networks
The ability to control emergent phenomena depends on decomposingthem into aspects susceptible to independent engineering. Forspatial self-managing systems, the amorphous-medium abstraction lets youseparate the systemÂs specification from its implementation
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
- …